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Saliency Detection with Multi-Scale Superpixels

Saliency Detection with Multi-Scale Superpixels
Saliency Detection with Multi-Scale Superpixels

Saliency Detection with Multi-Scale Superpixels Na Tong,Huchuan Lu,Lihe Zhang,and Xiang Ruan

Abstract—We propose a salient object detection algorithm via multi-scale analysis on superpixels.First,multi-scale seg-mentations of an input image are computed and represented by superpixels.In contrast to prior work,we utilize various Gaussian smoothing parameters to generate coarse or?ne results,thereby facilitating the analysis of salient regions.At each scale,three essential cues from local contrast,integrity and center bias are considered within the Bayesian framework.Next,we compute saliency maps by weighted summation and normalization.The ?nal saliency map is optimized by a guided?lter which further improves the detection results.Extensive experiments on two large benchmark datasets demonstrate the proposed algorithm performs favorably against state-of-the-art methods.The pro-posed method achieves the highest precision value of97.39%when evaluated on one of the most popular datasets,the ASD dataset. Index Terms—Multi-scale analysis,saliency map,visual saliency.

I.I NTRODUCTION

I T IS well known that animal vision systems can effortlessly

and ef?ciently distinguish salient regions from a cluttered scene,as it is a key attentional mechanism related to the basic survival skills.For computer vision systems,it is of great in-terest to reduce the computational load by focusing on the most salient regions for ef?cient and robust visual processing.As an important preprocessing step,saliency detection algorithms have found numerous applications including segmentation,ob-ject detection and object recognition,to name a few. Saliency models can be categorized as either bottom-up or top-down for two research directions[1]:human?xation pre-diction[2],[3]and salient object detection[4].In this work,we focus on bottom-up saliency models for object detection.The center-surround contrast[5]–[8]is one of the widely adopted principles.However,saliency algorithms[5],[6]based on this principle often highlight the pixels on the boundary rather than

Manuscript received January28,2014;revised April17,2014;accepted May09,2014.Date of publication May13,2014;date of current version May19,2014.This work was supported by the Joint Foundation of China Education Ministry and China Mobile Communication Corporation under Grant MCM20122071,and in part by the Fundamental Research Funds for the Central Universities under Grant DUT14YQ101and the Natural Science Foundation of China under Grant61371157.The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Giuseppe Scarpa.

N.Tong,H.Lu,and L.Zhang are with School of Information and Commu-nication Engineering,Dalian University of Technology,Dalian,116024,China (e-mail:tongna@https://www.sodocs.net/doc/1c11765460.html,;lhchuan@https://www.sodocs.net/doc/1c11765460.html,;zhanglihe@https://www.sodocs.net/doc/1c11765460.html,. cn).

X.Ruan is with the OMRON Corporation,Kusatsu-city,Shiga525-0035, Japan(e-mail:gen@omm.ncl.omron.co.jp).

Color versions of one or more of the?gures in this paper are available online at https://www.sodocs.net/doc/1c11765460.html,.

Digital Object Identi?er10.1109/LSP.2014.2323407those within the salient objects.The other one is information maximization principle which operates on the premise that pixels with the greatest entropy tend to be more prominent than others,which is usually employed on pixels independently without taking image structure into account,and are not able to uniformly highlight salient objects,i.e.,[9].

Considering all the above-mentioned issues,we propose a bottom-up saliency detection model based on the following properties:

?Structure.We exploit image structure for saliency detec-tion via superpixels to highlight salient pixels uniformly and ef?ciently.

?Region contrast.Local region contrast provides more vi-sual information than pixel-based contrast.

?Multi-scale analysis.We apply multi-scale analysis with multiple segmentations to handle size variation of salient objects.

?Integrity.Integrity is another vital factor as the contents of salient objects are usually smooth and undivided.?Center prior.Human vision systems tend to focus on the central region of a scene and thus the object appearing near the center is assigned to a higher weight.

?Filtering.The raw saliency detection results are usually not smooth enough within the foreground or the back-ground.Thus,an edge-preserved smoothing operator is in-troduced to further enhance salient detection results. Based on these properties,we propose a salient object detec-tion model based on multi-scale superpixel segmentations and the Bayesian framework.

The most related works are[16],[17].We use the improved version of the convex hull in[16],[17]for Bayesian inference. However,different from these works,we have four main con-tributions as follows.

?We introduce a novel multi-scale strategy by using various Gaussian smoothing parameters to incorporate precision of ?ne scales and integrity of coarse scales.

?We add integrity principle to the region contrast to make the saliency computation more reasonable and accurate.?We utilize the guided?lter to optimize the saliency maps, which further improve both the quantitative and qualitative results.

?We conclude six principles for effective saliency computa-tion and fuse them into a single framework where each part is complementary to others for achieving state-of-the-art results.

We use the Precision and Recall(P-R)curve and Area Under ROC Curve(AUC)to evaluate the proposed algorithm and 22state-of-the-art methods on two benchmark datasets.Fig.1 shows samples of saliency maps generated by state-of-the-art methods and our methods.Both quantitatively and qualitatively experimental results demonstrate that our algorithm performs favorably among all the evaluated methods,which bear out the validity of the principles used in the proposed saliency model.

1070-9908?2014IEEE.Personal use is permitted,but republication/redistribution requires IEEE permission.

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Fig.1.Saliency maps (from left to right,top to down):input,LC [10],SR [11],CBsal [12],GB [9],CA [6],HC [13],LRMR [14],SVO [15],RA10[7],RC [13],XL11[16],XL12[17],GS_SP [18],SF [19],HS [20],RC-J [1],GC [21],DSR [22],AMC [23],GMR [24],proposed algorithm without optimization,proposed algorithm and ground truth.The proposed algorithm highlights the salient object

uniformly.

Fig.2.Examples of multi-scale superpixels with various Gaussian smoothing and scale parameters.(a)Input (b)(c)

(d)(e)(f).

II.S ALIENCY VIA M ULTI -S CALE S UPERPIXELS

The proposed approach is formulated based on multi-scale superpixels as they encode compact and structural information within a scene.As for single-scale superpixel based method,the ?nal results will be affected directly by the accuracy of the segmentation method.Multi-scale analysis can synthesize image information of multiple scales,incorporating precision of ?ne scales and integrity of coarse scales,which makes it perfectly ?t for unsupervised or bottom-up methods in image processing.In contrast with the traditional multi-scale methods which formulate low-resolution saliency maps,a different ap-proach is adopted by over-segmenting an image with different scale parameters at the original resolution of the image,which is proved valid experimentally.In this study,we use an ef ?-cient graph-based segmentation algorithm [25]to generate su-perpixels.These superpixels are generated with various scales and smoothing parameters,and ,where is the Gaussian smoothing parameter and controls the region size.A.Single-scale Saliency

1)Image Features:Based on superpixels,we consider region contrast,center-bias and integrity to compute saliency maps.We construct two feature spaces for each superpixel at a scale ,i.e.,a quanti ?ed histogram in the CIE LAB color space and a vector of ,where and denote the average position of pixels superpixel and are normalized to [0,1],and indicates the number of pixels that lie on the image boundary.Take the upper right image of Fig.2(f)for example.The blue superpixel on the top has larger value of as it has numerous pixels on the image boundary whereas the white superpixel has zero value of .Likewise,the central object of the lower right image has zero value of .

2)Saliency Measure:We de ?ne a function to measure the saliency of a superpixel based on three simple but essential prin-ciples discussed in Section I.First,a superpixel of higher con-trast with neighbors should have higher saliency value.Second,a region closer to the image center is more likely to be salient

(i.e.,based on center prior [4],[26],[27]).In addition,we ob-serve that a region with a large number of pixels on the image boundary is likely to belong to the background.Therefore,we take the number of pixels on the image boundary into account and de ?ne the integrity of a region based on that.An input image is over-segmented at scales (e.g.,

in this work).At any scale ,an image is segmented into superpixels ,where is the number

of regions.Given a superpixel

and its neighboring regions ,where is the number of its neigh-bors.We de ?ne the of as:

(1)

where

is the ratio of the neighbor region to the total area of its neighborhood

,and is the histogram dis-tance computed simply using the Euclidean metric.The function

ensures that the output value is positive,and we use

(2)

to weigh highly salient regions more in this work.In Eq.(1),

computes the normalized spatial distance between the

of the superpixel and the image center .It is de ?ned by

(3)

where and are set as one third of the width and the height of the image Therefore,the saliency value of the superpixel closer to the center is assigned to a higher weight.The integrity of a superpixel,,in Eq.(1),is de ?ned as:

(4)

where denotes the number of pixels on the image boundary that the superpixel contains,indicates the total number

TONG et al.:SALIENCY DETECTION WITH MULTI-SCALE SUPERPIXELS

1037

Fig.3.(a)(d)Convex hulls generated by[16].(b)(e)Superpixels.(c)(f)The foreground regions generated by using contours of superpixels.

of pixels on the boundary for an input image,controls the strength of its in?uence and is the threshold,i.e.,

.A superpixel with larger indicates less liable to be an integral object.When is zero,it means the region is not close to the image boundaries,and. Otherwise is a positive value bounded within

Given an image,we?rst compute a prior map based on su-perpixels using Eq.(1)for Bayesian inference.

3)Bayesian Enhancement:The Bayesian framework is

a probabilistic model which makes an optimal decision by considering both the prior probability and the likelihood.In our approach,we use the Bayesian framework[7],[16]to generate more stable and accurate saliency value for each pixel.For Bayesian inference,we need to compute both prior (Section II-A)and likelihood based on superpixels.As for the likelihood,we?rst construct a rough prominent region to enclose the salient points detected by the boosted Harris point operators[28],[29]after eliminating those points near the image boundary.Based on the coarse estimation from salient points,we further re?ne and obtain the salient foreground region of an image with fewer background pixels,thereby generating a more precise observation model.

As superpixels represent local structure information and the convex hull of interest points captures global salient region,we utilize both to extract the foreground region of an image,instead of the convex hull based region in[16].We label a superpixel as a part of the foreground region if its overlap ratio over the convex hull is above a pre-de?ned threshold.Since superpixels are extracted at multiple scales,we obtain the coarse foreground region in each scale from the superpixels in the corresponding layer.We note that this simple yet effective method performs well in practice,as shown in Fig.3.

The observation likelihood is computed based on the pixel-wise color histogram within the extracted foreground region. First,an image is represented by a color histogram where each pixel falls into a certain feature,which is the discrete value in three color channels in the CIE LAB color space.We use(or)to denote the foreground(or background),then indicates the bin which contains the feature.We de-?ne to represent the set of points that fall into the bin. Each pixel is represented by a vector in the CIE LAB color space.The observation of pixel in one color channel is de?ned as:

(5)

(6) Assuming the probability distribution of pixels whose features are at the same bin is constant within a superpixel according to [7],we can compute the integration above by simply counting the number of points that fall into a bin in(or)represented by(or),and the number of(or )take the place of(or).As for the three color channels,We consider them to be independent of each other and take the multiplication operation to compute the?nal likelihood. Therefore,Eq.(5)can be rewritten as:

(7)

(8) where and denote the total pixel numbers of the fore-ground and the background respectively,in-dicates the observation likelihood of pixel being salient while indicates the likelihood of pixel belonging to the As discussed above,the saliency measure of a superpixel is delivered to every pixel inside it.We set the prior probability of each pixel to be within the foreground as,which means the probability value of the superpixel pixel ,and that of the pixel to be within the background as

.Here is computed according to Eq.

in II-A.the saliency value of the pixel at the scale within Bayesian inference is de?ned by,

(9)

B.Integration and Optimization

With multi-scale analysis,we get saliency values for each pixel.Here in the proposed approach.The overall saliency map is constructed by weighted summation of values.The weights are determined by how similar a pixel is to the superpixel containing it.The similarity is measured using the Euclidean distance between the representation of a pixel z,,and the average of pixels within the superpixel.

For each pixel,there are values:

.We de?ne the overall saliency map by,

(10) where is the weight at each scale:

(11) where denotes the superpixel that the pixel belongs to and is the average of pixels within the super-

1038IEEE SIGNAL PROCESSING LETTERS,VOL.21,NO.9,SEPTEMBER

2014

https://www.sodocs.net/doc/1c11765460.html,parative results.The left two ?gures are the P-R curves on the ASD dataset and the right two ?gures are the P-R curves on the THUS dataset.

TABLE I

AUC ON THE ASD AND THUS D ATASETS .T HE B EST T HREE R ESULTS ARE S HOWN IN R ED ,B LUE AND G REEN F ONTS R

ESPECTIVELY

pixel,and is a small constant to avoid being divided by zero.

The variable

is a normalization factor for the pixel ,(12)

We further re ?ne the saliency map with the guided ?lter [30].We

adopt the saliency map from Eq.(10)as the guidance image to ?lter itself in order to generate smooth results and strong edges with less noise.

III.E XPERIMENTS AND R ESULTS

In this letter,we compare the proposed method with 22state-of-the-art saliency detection approaches on two publicly available datasets to demonstrate its superiority.The ASD dataset is a salient object dataset of 1000images selected from the MSRA dataset [27]with pixel-level ground truths [31].Furthermore,we use the THUS dataset [1]of 10000images with pixel-wise ground truth,also selected from the database provided by [27].For other algorithms,we use the implementations or the result maps provided by the authors for fair evaluation.All the experiments are run in the MATLAB platform on a PC with Intel i7-3770CPU (3.4GHz)and 32GB RAM.We will provide the code of our method on our project site.The 22compared methods on the ASD dataset are:IT98[5],GB [9],LC [10],SR [11],FT [31],CA [6],RA10[7],HC and RC [13],CBsal [12],XL11[16],SVO [15],SF [19],LRMR [14],XL12[17],GS_SP [18],GMR [24],AMC [23],GC [21],HS [20],RC-J [1]and DSR [22].

We show the comparative results of 20methods on the THUS dataset since the SF and GS_SP models only provide saliency maps on the ASD dataset.

1)Saliency maps:Fig.1shows the comparison of the saliency maps generated by 22methods including ours.The experiments show least difference between the saliency maps of the proposed method with the ground truth,which demonstrates the proposed method achieves signi ?cant improvement over previous methods.All the operations in the proposed approach enable our method to locate the object precisely,highlight the

salient object and simultaneously suppress the background effectively.Furthermore,our optimization method can further smooth the ?nal saliency map and conserve the boundary of the salient object.

2)Quantitative evaluation:For a saliency map with inten-sity values in the range between [0,255],we set the threshold from 0to 255with an increment of 5,obtaining 52binary masks for each image.Based on the ground truth,we compute the P-R curve.We also calculate the ROC and AUC based on true positive and false positive rates calculated during the computa-tion of P-R values.Since the AUC results are consistent with the ROC curves,we omit the ROC curves and only show the AUC values in Table I (“Our_nf”denotes the results generated by the proposed method before ?ltering),which indicates the proposed methods outperform previous methods in terms of the AUC values.Fig.4shows the P-R curves on the ASD and THUS datasets.The evaluation results demonstrate that the proposed approaches (both before and after optimizing)have competitive precision and recall curves when compared with state-of-the-art methods.For fair evaluation,we compare the proposed method with other approaches also equipped with our ?ltering measure using AUC as the evaluation criterion on the ASD dataset,as shown in the last row of Table I.The comparative results indi-cate that the proposed method still performs the best even after they are optimized using the ?ltering step in terms of AUC.IV .C ONCLUSION

In this paper,we propose a novel bottom-up saliency detec-tion model.On account of the 6principles stated in Section I,the proposed method carries out saliency detection via multi-scale analysis within the Bayesian framework.In this work,integrity is taken into consideration,which plays an important role in suppressing the background.We further introduce the guided ?lter into saliency detection for improvement.For assessment,our approach is evaluated on two benchmark datasets against 22state-of-the-art algorithms.The experimental results show that our approach is able to accurately detect and uniformly high-light the salient object,and simultaneously suppress the back-ground,yielding high quality saliency maps.The P-R curves,and AUC values demonstrate the proposed method performs fa-vorably against the state-of-the-art approaches.

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with的复合结构和独立主格结构

1. with+宾语+形容词。比如:. The boy wore a shirt with the neck open, showing his bare chest. 那男孩儿穿着一件衬衫,颈部敞开,露出光光的胸膛。Don’t talk with your mouth full. 嘴里有食物时不要讲话。 2. with+宾语+副词。比如:She followed the guide with her head down. 她低着头,跟在导游之后。 What a lonely world it will be with you away. 你不在,多没劲儿呀! 3. with+宾语+过去分词。比如:He was listening to the music with his eyes half closed. 他眼睛半闭着听音乐。She sat with her head bent. 她低着头坐着。 4. with+宾语+现在分词。比如:With winter coming, it’s time to buy warm clothes. 冬天到了,该买些保暖的衣服了。 He soon fell asleep with the light still burning. 他很快就睡着了,(可)灯还亮着。 5. with+宾语+介词短语。比如:He was asleep with his head on his arms. 他的头枕在臂膀上睡着了。 The young lady came in, with her two- year-old son in her arms. 那位年轻的女士进来了,怀里抱着两岁的孩子。 6. with+宾语+动词不定式。比如: With nothing to do in the afternoon, I went to see a film. 下午无事可做,我就去看了场电影。Sorry, I can’t go out with all these dishes to wash. 很抱歉,有这么多盘子要洗,我不能出去。 7. with+宾语+名词。比如: He died with his daughter yet a school-girl.他去逝时,女儿还是个小学生。 He lived a luxurious life, with his old father a beggar . 他过着奢侈的生活,而他的老父亲却沿街乞讨。(8)With so much work to do ,I can't go swimming with you. (9)She stood at the door,with her back towards us. (10)He entered the room,with his nose red with cold. with复合结构与分词做状语有啥区别 [ 标签:with, 复合结构, 分词状语] Ciro Ferrara 2009-10-18 16:17 主要是分词形式与主语的关系 满意答案好评率:100%

with复合结构专项练习96126

with复合结构专项练习(二) 一请选择最佳答案 1)With nothing_______to burn,the fire became weak and finally died out. A.leaving B.left C.leave D.to leave 2)The girl sat there quite silent and still with her eyes_______on the wall. A.fixing B.fixed C.to be fixing D.to be fixed 3)I live in the house with its door_________to the south.(这里with结构作定语) A.facing B.faces C.faced D.being faced 4)They pretended to be working hard all night with their lights____. A.burn B.burnt C.burning D.to burn 二:用with复合结构完成下列句子 1)_____________(有很多工作要做),I couldn't go to see the doctor. 2)She sat__________(低着头)。 3)The day was bright_____.(微风吹拂) 4)_________________________,(心存梦想)he went to Hollywood. 三把下列句子中的划线部分改写成with复合结构。 1)Because our lessons were over,we went to play football. _____________________________. 2)The children came running towards us and held some flowers in their hands. _____________________________. 3)My mother is ill,so I won't be able to go on holiday. _____________________________. 4)An exam will be held tomorrow,so I couldn't go to the cinema tonight. _____________________________.

With的用法全解

With的用法全解 with结构是许多英语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述,以帮助同学们掌握这一重要的语法知识。 一、 with结构的构成 它是由介词with或without+复合结构构成,复合结构作介词with或without的复合宾语,复合宾语中第一部分宾语由名词或代词充当,第二部分补足语由形容词、副词、介词短语、动词不定式或分词充当,分词可以是现在分词,也可以是过去分词。With结构构成方式如下: 1. with或without-名词/代词+形容词; 2. with或without-名词/代词+副词; 3. with或without-名词/代词+介词短语; 4. with或without-名词/代词 +动词不定式; 5. with或without-名词/代词 +分词。 下面分别举例: 1、 She came into the room,with her nose red because of cold.(with+名词+形容词,作伴随状语)

2、 With the meal over , we all went home.(with+名词+副词,作时间状语) 3、The master was walking up and down with the ruler under his arm。(with+名词+介词短语,作伴随状语。) The teacher entered the classroom with a book in his hand. 4、He lay in the dark empty house,with not a man ,woman or child to say he was kind to me.(with+名词+不定式,作伴随状语)He could not finish it without me to help him.(without+代词 +不定式,作条件状语) 5、She fell asleep with the light burning.(with+名词+现在分词,作伴随状语) Without anything left in the with结构是许多英 语复合结构中最常用的一种。学好它对学好复合宾语结构、不定式复合结构、动名词复合结构和独立主格结构均能起很重要的作用。本文就此的构成、特点及用法等作一较全面阐述,以帮助同学们掌握这一重要的语法知识。 二、with结构的用法 with是介词,其意义颇多,一时难掌握。为帮助大家理清头绪,以教材中的句子为例,进行分类,并配以简单的解释。在句子中with结构多数充当状语,表示行为方式,伴随情况、时间、原因或条件(详见上述例句)。 1.带着,牵着…… (表动作特征)。如: Run with the kite like this.

高中英语独立主格结构、with的复合结构专项练习测试40题(有答案)

一、选择题 1、With time ____ by , they got to know each other better. A. passes B. passing C. passed D. to be passed 2、 the economic crisis getting more and more serious, the government is searching for ways to improve people’s life. A. As B. With C. When D. If 3John received an invitation to dinner, and with his work ____, he gladly accepted it. A. finished B. finishing C. having finished D. was finished 4、With all flights___, they had to come by bus. A. had canceled B.canceled C.have been canceled D. having canceled 5、With a large number of people _______ camping, it has now become one of the most popular activities in the UK. A. take part in B. took part in C. taking part in D. to be taking part in 6、None of us had expected that the middle﹣aged engineer died with his design _________() A..to uncomplete B..uncompleted C.uncompleting.D..uncomplete 7、______,we managed to get out of the forest.() A.The guide led the way B.The guide leading the way C.With the guide to lead the way D.Having led the way 8、Will all his work ,he could have a good rest. A.to do B.doing C.did D.done 9、 ______, her suggestion is of greater value than yours. A. All things considering B. All things considered C. All things were considered D. With all things were considered 10、With the kind boy ________ the way, we found the park soon. A. leads B. to lead C. led D. leading 11、 She stood there, ______ from her cheeks. A. tears' rolling down B. tears rolled down C. with tears rolled down D. tears rolling down 12、 While watching television, __________. A. the doorbell rang B. the doorbell rings C. we heard the doorbell ring D. we heard the doorbell rings 13、The murderer was brought in, with his hands______ behind his back. A. be tied B. having tied C. to be tied D. tied 14、 With a lot of difficult problem _____, the newly-elected president is having a hard time.

5种基本句型和独立主格结构讲解

英语中的五种基本句型结构 一、句型1:Subject (主语) +Verb (谓语) 这种句型中的动词大多是不及物动词,所谓不及物动词,就是这种动词后不可以直接接宾语。常见的动词如:work, sing, swim, fish, jump, arrive, come, die, disappear, cry, happen等。如: 1) Li Ming works very hard.李明学习很努力。 2) The accident happened yesterday afternoon.事故是昨天下午发生的。 3)Spring is coming. 4) We have lived in the city for ten years. 二、句型2:Subject (主语) +Link. V(系动词) +Predicate(表语) 这种句型主要用来表示主语的特点、身份等。其系动词一般可分为下列两类: (1)表示状态。这样的词有:be, look, seem, smell, taste, sound, keep等。如: 1) This kind of food tastes delicious.这种食物吃起来很可口。 2) He looked worried just now.刚才他看上去有些焦急。 (2)表示变化。这类系动词有:become, turn, get, grow, go等。如: 1) Spring comes. It is getting warmer and warmer.春天到了,天气变得越来越暖和。 2) The tree has grown much taller than before.这棵树比以前长得高多了。 三、句型3:Subject(主语) +V erb (谓语) +Object (宾语) 这种句型中的动词一般为及物动词, 所谓及物动词,就是这种动词后可以直接接宾语,其宾语通常由名词、代词、动词不定式、动名词或从句等来充当。例: 1) He took his bag and left.(名词)他拿着书包离开了。 2) Li Lei always helps me when I have difficulties. (代词)当我遇到困难时,李雷总能给我帮助。 3) She plans to travel in the coming May Day.(不定式)她打算在即将到来的“五一”外出旅游。 4) I don’t know what I should do next. (从句)我不知道下一步该干什么。 注意:英语中的许多动词既是及物动词,又是不及物动词。 四、句型4:Subject(主语)+Verb(谓语)+Indirect object(间接宾语)+Direct object (直接宾语) 这种句型中,直接宾语为主要宾语,表示动作是对谁做的或为谁做的,在句中不可或缺,常常由表示“物”的名词来充当;间接宾语也被称之为第二宾语,去掉之后,对整个句子的影响不大,多由指“人”的名词或代词承担。引导这类双宾语的常见动词有:buy, pass, lend, give, tell, teach, show, bring, send等。如: 1) Her father bought her a dictionary as a birthday present.她爸爸给她买了一本词典作为生日礼物。 2)The old man always tells the children stories about the heroes in the Long March. 老人经常给孩子们讲述长征途中那些英雄的故事。上述句子还可以表达为: 1)Her father bought a dictionary for her as a birthday present. 2)The old man always tells stories about the heroes to the children in the Long March. 五、句型5:Subject(主语)+Verb (动词)+Object (宾语)+Complement(补语) 这种句型中的“宾语+补语”统称为“复合宾语”。宾语补足语的主要作用或者是补充、说明宾语的特点、身份等;或者表示让宾语去完成的动作等。担任补语的常常是名词、形容词、副词、介词短语、分词、动词不定式等。如: 1)You should keep the room clean and tidy. 你应该让屋子保持干净整洁。(形容词) 2) We made him our monitor.(名词)我们选他当班长。 3) His father told him not to play in the street.(不定式)他父亲告诉他不要在街上玩。

with用法归纳

with用法归纳 (1)“用……”表示使用工具,手段等。例如: ①We can walk with our legs and feet. 我们用腿脚行走。 ②He writes with a pencil. 他用铅笔写。 (2)“和……在一起”,表示伴随。例如: ①Can you go to a movie with me? 你能和我一起去看电影'>电影吗? ②He often goes to the library with Jenny. 他常和詹妮一起去图书馆。 (3)“与……”。例如: I’d like to have a talk with you. 我很想和你说句话。 (4)“关于,对于”,表示一种关系或适应范围。例如: What’s wrong with your watch? 你的手表怎么了? (5)“带有,具有”。例如: ①He’s a tall kid with short hair. 他是个长着一头短发的高个子小孩。 ②They have no money with them. 他们没带钱。 (6)“在……方面”。例如: Kate helps me with my English. 凯特帮我学英语。 (7)“随着,与……同时”。例如: With these words, he left the room. 说完这些话,他离开了房间。 [解题过程] with结构也称为with复合结构。是由with+复合宾语组成。常在句中做状语,表示谓语动作发生的伴随情况、时间、原因、方式等。其构成有下列几种情形: 1.with+名词(或代词)+现在分词 此时,现在分词和前面的名词或代词是逻辑上的主谓关系。 例如:1)With prices going up so fast, we can't afford luxuries. 由于物价上涨很快,我们买不起高档商品。(原因状语) 2)With the crowds cheering, they drove to the palace. 在人群的欢呼声中,他们驱车来到皇宫。(伴随情况) 2.with+名词(或代词)+过去分词 此时,过去分词和前面的名词或代词是逻辑上的动宾关系。

with独立主格结构

with独立主格结构(即with复合结构) with独立主格结构是英语中一种重要的句法现象,在句子结构方面具有相对独立的特点。多年来也一直是命题的热点、重点,因此应该引起我们的高度重视。众所周知,with引导的独立主格结构非常活跃,虽然它在句子中只作状语,但是可以表示伴随、方式、原因、结果等各种复杂的情况。 现将with引导的独立主格结构总结如下。 一、句法结构 【结构一】 with +名词(代词)+介词短语 例1 He sat there thinking, with his chin on his hand. 他手托下巴,坐在那儿沉思。 【结构二】 with +名词(代词)+形容词 例2 He stared at his friend with his mouth wide open. 他张大嘴巴凝视着他的朋友。 【结构三】with +名词(代词)+副词 例3 With production up by 60%, the company has had another excellent year. 产量上升了60%, 公司又是一个好年景。 【结构四】 with +名词(代词)+名词 例4 She used to sit reading in the evening with her pet dog her only companion. 她从前总爱在晚上坐着看书,她的宠物狗便是她唯一的伙伴。 【结构五】with +名词(代词)+现在分词 例5 She stood there chatting with her friend, with her child playing beside her. 她站在那儿跟朋友闲聊,孩子在旁边玩。 【结构六】with +名词(代词)+过去分词

独立主格结构练习题及解析

独立主格结构练习题及解析 1. I have a lot of books, half of ___ novels. A. which B. that C. whom D. them 2. __ more and more forests destroyed, many animals are facing thedanger of dying out. A. because B. as C. With D. Since 3. The bus was crowded with passengers going home from market, most of __ carrying heavy bags and baskets full of fruit and vegetables they hadbought there. A. them B. who C. whom D. which 4. The largest collection ever found in England was one of about 200,000 silverpennies, all of ___ over 600 years old. A. which B. that C. them

D. it 5. The cave __ very dark, he lit some candles ___ light. A. was; given B. was; to give C. being; given D. being; to give 6. The soldier rushed into the cave, his right hand __ a gun and his face ____ with sweat.A held; covered B. holding; covering C. holding; covered D. held; covering 7. The girl in the snapshot was smiling sweetly, her long hair ___ . A. flowed in the breeze B. was flowing in the breeze C. were flowing in the breeze D. flowing in the breeze 8. The children went home from the grammar school, their lessons ____ for the day. A. finishing B. finished C. had finished D. were finished 9. On Sundays there were a lot of children playing in the park, ___ parents seated together joking.

独立主格with用法小全

独立主格篇 独立主格,首先它是一个“格”,而不是一个“句子”。在英语中任何一个句子都要有主谓结构,而在这个结构中,没有真正的主语和谓语动词,但又在逻辑上构成主谓或主表关系。独立主格结构主要用于描绘性文字中,其作用相当于一个状语从句,常用来表示时间、原因、条件、行为方式或伴随情况等。除名词/代词+名词、形容词、副词、非谓语动词及介词短语外,另有with或without短语可做独立主格,其中with可省略而without不可以。*注:独立主格结构一般放在句首,表示原因时还可放在句末;表伴随状况或补充说明时,相当于一个并列句,通常放于句末。 一、独立主格结构: 1. 名词/代词+形容词 He sat in the front row, his mouth half open. Close to the bank I saw deep pools, the water blue like the sky. 靠近岸时,我看见几汪深池塘,池水碧似蓝天。 2. 名词/代词+现在分词 Winter coming, it gets colder and colder. The rain having stopped, he went out for a walk.

The question having been settled, we wound up the meeting. 也可以The question settled, we wound up the meeting. 但含义稍有差异。前者强调了动作的先后。 We redoubled our efforts, each man working like two. 我们加倍努力,一个人干两个人的活。 3. 名词/代词+过去分词 The job finished, we went home. More time given, we should have done the job much better. *当表人体部位的词做逻辑主语时,不及物动词用现在分词,及物动词用过去分词。 He lay there, his teeth set, his hands clenched, his eyes looking straight up. 他躺在那儿,牙关紧闭,双拳紧握,两眼直视上方。 4. 名词/代词+不定式 We shall assemble at ten forty-five, the procession to start moving at precisely eleven. We divided the work, he to clean the windows and I to sweep the floor.

独立主格结构图表解析

独立主格结构 一、概念 “独立主格结构”就是由一个相当于主语的名词或代词加上非谓语动词、形容词(副)词或介词短语构成的一种独立成分。该结构不是句子,也不是从句,所以它内部的动词不能考虑其时态、人称和数的变化,它与主句之间不能通过并列连词连接,也不能由从句阴道词引导,通常用逗号与主句隔开。独立主格结构在很多情况下可以转化为相应的状语从句或者其他状语形式,但很多时候不能转化为分词形式,因为它内部动词的逻辑主语与主句主语不一致。 二、独立主格的特点

1.当独立主格结构中的being done表示“正在被做时”,being不可以被省略。 2.当独立主格结构的逻辑主语是it, there时,being不可以省略。 三、独立主格结构的用法。 一般放在句首,表示原因时还可放在句末;表伴随状况或补充说明时,相当于一个并列句,通常放于句末。

四、非谓语动词独立主格结构。 “名词或代词+非谓语动词”结构构成的独立主格结构称为非谓语动词的独立主格结构。名词或代词和非谓语动词具有逻辑上的主谓关系。 1.不定式构成的独立主格结构 不定式构成的独立主格结构往往表示还未发生的行为或状态,在句中常 作原因状语,有时做条件状语。 Lots of homework to do, I have to stay home all day. 由于很多作业要做,我只好待在家里。 So many children to look after, the mother has to quit her job. 如此多的孩子要照顾,这个妈妈不得不辞掉她的工作。 2.动词+ing形式的独立主格结构 动词-ing形式的句中作状语时,其逻辑主语必须是主句的主语,否则就 是不正确的。动词-ing形式的逻辑主语与主句的主语不一致时,就应在 动词的-ing形式前加上逻辑主语,构成动词-ing 形式的独立主格结构,逻辑主语与动词间为主谓关系,是分词的动作执行者,分词表示的动作 时逻辑主语发出的动作。 We redoubled our efforts, each man working like two. 我们加倍努力,每个人就像在干两个人的活。 The governor considering the matter, more strikers gathered across his path. 总督思考这个问题时,更多的罢工工人聚集到他要通过的路上。 The guide leading the way, we had no trouble getting out of the forest. 在向导的带领下,我们轻松地走出了森林。 3.过去分词形式的独立主格 过去分词形式的独立主格结构是由“逻辑主语+过去分词”构成。逻辑主 语与动词之间为动宾关系,它是分词的动作承受者,这一结构在句中作 时间状语,原因状语、伴随状语、条件状语等。 This done, we went home.做完这个,我们就回家了。 All our savings gone, we started looking for jobs. 积蓄用完后,我们都开始找工作。 More time and money given, we can finish the work in advance. 如果给予更多的时间和金钱,我们能提前完成这个工作。 五、其他形式的独立主格结构

with复合宾语的用法(20201118215048)

with+复合宾语的用法 一、with的复合结构的构成 二、所谓"with的复合结构”即是"with+复合宾语”也即"with +宾语+宾语补足语” 的结构。其中的宾语一般由名词充当(有时也可由代词充当);而宾语补足语则是根据 具体的需要由形容词,副词、介词短语,分词短语(包括现在分词和过去分词)及不定式短语充当。下面结合例句就这一结构加以具体的说明。 三、1、with +宾语+形容词作宾补 四、①He slept well with all the windows open.(82 年高考题) 上面句子中形容词open作with的宾词all the windows的补足语, ②It' s impolite to talk with your mouth full of food. 形容词短语full of food 作宾补。Don't sleep with the window ope n in win ter 2、with+宾语+副词作宾补 with Joh n away, we have got more room. He was lying in bed with all his clothes on. ③Her baby is used to sleeping with the light on.句中的on 是副词,作宾语the light 的补足语。 ④The boy can t play with his father in.句中的副词in 作宾补。 3、with+宾语+介词短语。 we sat on the grass with our backs to the wall. his wife came dow n the stairs,with her baby in her arms. They stood with their arms round each other. With tears of joy in her eyes ,she saw her daughter married. ⑤She saw a brook with red flowers and green grass on both sides. 句中介词短语on both sides 作宾语red flowersandgreen grass 的宾补, ⑥There were rows of white houses with trees in front of them.,介词短语in front of them 作宾补。 4、with+宾词+分词(短语 这一结构中作宾补用的分词有两种,一是现在分词,二是过去分词,一般来说,当分词所表 示的动作跟其前面的宾语之间存在主动关系则用现在分词,若是被动关系,则用过去分词。 ⑦In parts of Asia you must not sit with your feet pointing at another person.(高一第十课),句中用现在分词pointing at…作宾语your feet的补足语,是因它们之间存在主动关系,或者说point 这一动作是your feet发出的。 All the after noon he worked with the door locked. She sat with her head bent. She did not an swer, with her eyes still fixed on the wall. The day was bright,with a fresh breeze(微风)blowing. I won't be able to go on holiday with my mother being ill. With win ter coming on ,it is time to buy warm clothes. He soon fell asleep with the light still bur ning. ⑧From space the earth looks like ahuge water covered globe,with a few patches of land stuk ing out above the water而在下面句子中因with的宾语跟其宾补之间存在被动关系,故用过去分词作宾补:

with用法小结

with用法小结 一、with表拥有某物 Mary married a man with a lot of money . 马莉嫁给了一个有着很多钱的男人。 I often dream of a big house with a nice garden . 我经常梦想有一个带花园的大房子。 The old man lived with a little dog on the lonely island . 这个老人和一条小狗住在荒岛上。 二、with表用某种工具或手段 I cut the apple with a sharp knife . 我用一把锋利的刀削平果。 Tom drew the picture with a pencil . 汤母用铅笔画画。 三、with表人与人之间的协同关系 make friends with sb talk with sb quarrel with sb struggle with sb fight with sb play with sb work with sb cooperate with sb I have been friends with Tom for ten years since we worked with each other, and I have never quarreled with him . 自从我们一起工作以来,我和汤姆已经是十年的朋友了,我们从没有吵过架。 四、with 表原因或理由 John was in bed with high fever . 约翰因发烧卧床。 He jumped up with joy . 他因高兴跳起来。 Father is often excited with wine . 父亲常因白酒变的兴奋。 五、with 表“带来”,或“带有,具有”,在…身上,在…身边之意

With 引导的独立主格结构

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