iccv iccv2013 iccv2013-383 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Zhengxiang Wang, Rujie Liu
Abstract: This paper introduces to use semi-supervised learning for large scale image cosegmentation. Different from traditional unsupervised cosegmentation that does not use any segmentation groundtruth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared between the large number of unsegmented images. This would be a much practical way to effectively cosegment a large number of related images simultaneously, where previous unsupervised cosegmentation work poorly due to the large variances in appearance between different images and the lack ofsegmentation groundtruthfor guidance in cosegmentation. For semi-supervised cosegmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intraimage distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each subproblem for computation efficiency. Experiment results on iCoseg and Pascal VOC datasets show that the proposed cosegmentation method can effectively cosegment hundreds of images in less than one minute. And our semi-supervised cosegmentation is able to outperform both unsupervised cosegmentation as well asfully supervised single image segmentation, especially when the training data is limited.
Reference: text
sentIndex sentText sentNum sentScore
1 Different from traditional unsupervised cosegmentation that does not use any segmentation groundtruth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared between the large number of unsegmented images. [sent-5, score-1.959]
2 For semi-supervised cosegmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intraimage distance and the balance term. [sent-7, score-0.832]
3 We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each subproblem for computation efficiency. [sent-8, score-0.497]
4 Experiment results on iCoseg and Pascal VOC datasets show that the proposed cosegmentation method can effectively cosegment hundreds of images in less than one minute. [sent-9, score-1.034]
5 And our semi-supervised cosegmentation is able to outperform both unsupervised cosegmentation as well asfully supervised single image segmentation, especially when the training data is limited. [sent-10, score-1.446]
6 Introduction The problem of image cosegmentation is actively studied in recent computer vision community. [sent-12, score-0.683]
7 Given a set of related images with the prior knowledge that they all contain a common object, the goal of cosegmentation is to automatically find this common object in each image and segment it as foreground. [sent-13, score-0.769]
8 The original cosegmentation studies [24, 19, 20, 11, 26] could only handle just a pair of images. [sent-15, score-0.69]
9 Recent studies [13, 5, 27, 21, 14, 25, 22] extend this limitation and can cosegment multiple images. [sent-16, score-0.328]
10 [17, 15] have tried to cosegment hundreds of or even thousands of images, but they use clustering strategy that divides the large image set into multiple subsets, and then cosegment each subset separately. [sent-20, score-0.613]
11 This may not be an optimal solution as it avoids to directly cosegment the whole image set, and the similarity information (about the common object) between images in different subsets is lost. [sent-21, score-0.408]
12 In this paper, we try to cosegment a large number of images simultaneously, which is a much challenging task due to the large variance between different images. [sent-22, score-0.338]
13 If some training image foregrounds are provided, it is possible to guide the cosegmentation task towards a correct direction. [sent-23, score-0.865]
14 It 393 uses training image foregrounds for guidance in cosegmentation, and exploits the similarity from both the training images and unsegmented images. [sent-28, score-0.72]
15 The intra-image distance considers spatial continuity within each unsegmented image. [sent-29, score-0.399]
16 With these three terms, the resulting energy minimization problem can be formulated as a binary quadratic programming (QP) problem, which is able to effectively segment the foreground of each unsegmented image. [sent-31, score-0.749]
17 To increase efficiency, we propose an iterative updating algorithm using the trust region idea to solve the energy function. [sent-33, score-0.342]
18 That is, we update every image one by one alternatively in each iteration, by keeping the foregrounds of other images fixed and updating the foreground of each image as a sub-problem. [sent-34, score-0.479]
19 Compared to updating all images simultaneously using only one iteration, this iterative updating algorithm can significantly reduce the number of variables in each sub-problem and therefore speed up the whole procedure. [sent-36, score-0.444]
20 For cosegmenting hundreds of images, only less than one minute is required by using this iterative updating algorithm. [sent-38, score-0.517]
21 After solving the above mentioned accuracy and efficiency issues in cosegmenting a large number of images, our proposed semi-supervised method is more practical for real world applications than previous cosegmentation works. [sent-39, score-0.869]
22 We propose an effective method for semi-supervised cosegmentation by minimizing an energy fiu-snucptieornv itsheadt consists of the inter-image distance, the intra-image distance and the balance term. [sent-41, score-0.832]
23 We propose an efficient algorithm to solve the energy fWunec ptiroonp by i atnera eftifvieci updating, hwmhi ctho siso lavbele t htoe cosegment hundreds of image in less than one minute. [sent-42, score-0.436]
24 We describe our energy function and the iterative updating algorithm in Section 3, and evaluate its performance in Section 4. [sent-45, score-0.305]
25 Related Work Image Cosegmentation: The problem of cosegmentation is firstly proposed by Rother et al. [sent-48, score-0.684]
26 [24], in which a common object shared by two images is segmented by measuring the similarity between their foreground histograms with L1-norm. [sent-49, score-0.265]
27 The resulting foregrounds by cosegmentation would be helpful in many other applications. [sent-50, score-0.82]
28 [24] show that the distance between an image pair measured by the cosegmented foregrounds can help improve image retrieval. [sent-52, score-0.251]
29 Due to its usefulness in other computer vision applications, cosegmentation has been actively studied in recent years. [sent-55, score-0.683]
30 Recent works [13, 5, 27, 21, 25] extend previous limitation of cosegmenting only two images, and can cosegment multiple images. [sent-57, score-0.507]
31 The work in [17, 14, 22] also extend the foreground-background binary segmentation to multiple regions, which is able to cosegment multiple images with multiple objects. [sent-58, score-0.413]
32 Another recent work in [16] tries to cosegment with multiple foregrounds, which would be a more challenging problem. [sent-59, score-0.294]
33 All these works are unsupervised and limited to cosegment at most dozens of images simultaneously. [sent-60, score-0.422]
34 For segmenting large scale dataset, [17, 15] use clustering strategy to divide the large image set into multiple subsets, and cosegment each subset separately. [sent-61, score-0.297]
35 [18] trans- fers segmentations from segmented images in the current source set to unsegmented images in the next target set by segmentation propagation, and finally segment the whole ImageNet dataset [8]. [sent-62, score-0.583]
36 However, these works do not cosegment all images simultaneously, and may lose the similarity information between images in different subsets. [sent-63, score-0.382]
37 These methods are unable to effectively and efficiently cosegment a large number of images, which would be benefit by semi-supervised learning. [sent-71, score-0.275]
38 For this task, superpixels are firstly extracted from each image in pre-processing, so that the foreground/background label can be defined on each superpixel rather than on each pixel for computation efficiency. [sent-75, score-0.398]
39 For each training image, the label of each superpixel can be easily determined by comparing the areas covered by foreground and background. [sent-76, score-0.393]
40 For each unsegmented image, this task is formulated as predicting the label for each superpixel, then the final foreground can be found by selecting superpixels with foreground labels. [sent-77, score-0.734]
41 A vector 푦푖 is used to represent the superpixel labels for an image 푋푖, with the dimension 푠푖 equal to the number of superpixels in this image. [sent-78, score-0.394]
42 Each component 푦푖 (푗) in vector 푦푖 is a binary variable, with 1indicating the corresponding superpixel belongs to foreground and 0 for background. [sent-79, score-0.367]
43 The determination of 푦푖 for each unsegmented image is formulated as an energy function minimization problem, which is then solved by an iterative updating algorithm. [sent-80, score-0.771]
44 Like many previous works [24, 20, 11, 26, 21, 5, 15] , histogram descriptors are used to × represent superpixels and the foregrounds of images, which can be either bag-of-words histogram with some local features, or color histogram based on pixel intensities. [sent-84, score-0.365]
45 The superpixel histogram is represented by ℎ푖 (푗) ∈ 푅푑 for each superpixel 푗 in image 푋푖, and the foreground histogram of image 푋푖 can be calculated as ∑푗 푦푖 (푗) ⋅ ℎ푖 (푗), which can also be formulated as 퐻푖 ⋅ 푦푖, w∑here 퐻(푗푖 )is ⋅ a (푑 푠푖) matrix with each column correspondin∑g to ℎ푖 (푗i)s. [sent-85, score-0.694]
46 1 Energy function definition The proposed energy function is composed of three terms: the inter-image distance, the intra-image distance and the balance term, in which all unsegmented images are included. [sent-88, score-0.604]
47 Therefore by solving the minimizing problem with this energy function, the superpixel labels of all unsegmented images can be calculated simultaneously. [sent-89, score-0.825]
48 The inter-image distance measures the similarity of foregrounds between different images, including the similarity between unsegmented images and training images as well as that between pair-wise unsegmented images. [sent-90, score-1.112]
49 Therefore both the training segmentation groundtruth and the similarity information shared between unsegmented images are explored in the inter-image distance. [sent-91, score-0.655]
50 The intra-image distance considers the spatial consistency between two adjacent superpixels inside an unsegmented image. [sent-93, score-0.551]
51 , foreground or background, by adding a penalty to the energy function in case that two adjacent superpixels are given different labels. [sent-96, score-0.345]
52 Here 훼(푗, 푘) is the shared edge length between two superpixels 푗 and 푘, 푁(푗) is the set of adjacent superpixels of 푗, and 휃 is a constant value, which is set as the variance of the distance values between all superpixel histograms. [sent-99, score-0.568]
53 The balance term prevents all superpixels belonging to the same label during the energy minimization procedure. [sent-100, score-0.366]
54 By summing nth beese ca lthcurelea terms, t1he − w 푃hole energy function can be formulated as: 퐸 × = 퐸푖푛푡푒푟 + 휆1 ⋅ 퐸푖푛푡푟푎 + 휆2 ⋅ 퐸푏푎푙 (7) where 휆1 and 휆2 are two trade-off parameters to control the proportion of each term in the energy function. [sent-103, score-0.304]
55 2 Binary quadratic programming problem Given the definition of the energy function, the minimization can be converted to a binary QP problem, by reformulating each of the three terms into suitable form. [sent-106, score-0.274]
56 Its diagonal component 푀푖푖푛푡푟푎 (푗, 푗×) i푠s calculated as: 푀푖푖푛푡푟푎(푗,푗) = ∑ 푘∈∑푁(푗) (푊푖(푗, 푘) + 푊푖(푘,푗)) (14) and the off-diagonal component 푀푖푖푛푡푟푎 (푗, 푘) is calculated as follows if superpixel 푗 and 푘 are adjacent, or 0 otherwise. [sent-111, score-0.313]
57 Iterative updating algorithm The binary QP problem has been studied extensively in the optimization literature [2, 23, 20], and Equation 18 can be easily solved using these methods when cosegmenting a small number of images. [sent-116, score-0.428]
58 To increase efficiency, we propose an iterative updating algorithm using the trust region idea to solve 푌 396 this problem. [sent-118, score-0.244]
59 The basic idea of this algorithm is to update every unsegmented image one by one alternatively in each iteration, by keeping the superpixel labels of other images fixed in updating the current image, and repeat this iteration until convergence. [sent-119, score-0.884]
60 In this way, updating the superpixel labels of each image is decomposed as a sub-problem, where the number of variables (superpixel labels) is significantly reduced and the optimization procedure can be accelerated. [sent-120, score-0.436]
61 In the experiment of this paper, we simply relax the binary variable of each superpixel label 푦푖 (푗) from {0, 1} to [0, 1] . [sent-123, score-0.292]
62 The resulting value is then rounded to binary value for superpixel labels. [sent-125, score-0.301]
63 In the iterative updating algorithm, all sub-problems are solved individually to update the superpixel labels of the corresponding images. [sent-126, score-0.501]
64 In two successive iterations, the only difference in updating each image 푋푖 of subproblem 퐸푖 is that the labels of other unsegmented ima∑ges 푦푗 would be changed, therefore only the first term 푀푖푖푗푛푡푒푟 ⋅ 푦푗) in vector 푉푖′ (Equation 22) of each s∑ub-problem is required to be re-calculated. [sent-127, score-0.626]
65 As this term n∑eeds to sum over all other images, the complexity of updating all images grows quadratically with the number of images, which seems inefficient for large scale cosegmentation. [sent-128, score-0.259]
66 Another advantage of the iterative updating algorithm is that it can also reduce the rounding error compared to directly solving energy function of Equation 18 (where the superpixel labels of all images need to be rounded simultaneously). [sent-132, score-0.673]
67 This is because the rounding error of superpixel labels only occurs in the corresponding sub-problem and will be fixed in other sub-problems. [sent-133, score-0.299]
68 The proposed iterative updating algorithm is similar to trust region graph cut in [24]. [sent-135, score-0.244]
69 However, in our semi-supervised cosegmentation, the limited training images provide a good initialization and can guide the cosegmentation towards a correct direction for unsegmented images. [sent-138, score-1.135]
70 Moreover, each sub-problem is approximated as a convex QP problem, which makes the initialization for unsegmented images not important anymore. [sent-139, score-0.41]
71 We simply set all initial superpixel labels as 1. [sent-140, score-0.277]
72 iCoseg dataset is popularly used in previous cosegmentation works [1, 27, 25, 14], 397 Table 1. [sent-146, score-0.656]
73 Cosegmentation accuracy comparison in iCoseg dataset which contains 38 classes, each for an independent cosegmentation task. [sent-147, score-0.656]
74 However, most classes contain only a few images, therefore we select 10 representative classes containing more images for our cosegmentation experiment, in which the number of images ranges from 18 to 40. [sent-148, score-0.77]
75 For the representation of each superpixel and foreground, we use color histogram with RGB and Lab color channels. [sent-152, score-0.261]
76 The intersection-over-union score is used to measure the cosegmentation accuracy, which is a standard evaluation metric in Pascal Challenges [9]. [sent-153, score-0.656]
77 Three recent cosegmentation works [13, 17, 14] are compared in iCoseg dataset, which are implemented by their publicly available code with the default parameter iterative updating algorithm. [sent-157, score-0.863]
78 Note that although only 4 images are shown here as example, this is the intermediate result of cosegmenting all the 25 images in “Baseball” class in iCoseg dataset. [sent-158, score-0.293]
79 In [17] and [14], images can be cosegmented into multiple regions, therefore we adjust the number of regions K from 2 to 9 and report the best one, for the foregroundbackground binary cosegmentation in this experiment. [sent-160, score-0.793]
80 Table 1 shows the cosegmentation accuracy of each class and the average results. [sent-162, score-0.656]
81 Cosegmentation dataset accuracy comparison in VOC2012 ficult to determine the best K beforehand in unsupervised cosegmentation tasks. [sent-166, score-0.713]
82 An example of the intermediate result during our iterative updating algorithm is shown in Figure1, and an analysis of the cosegmentation accuracy affected by the choice of parameters (휆1 and 휆2) can be found in the supplementary material. [sent-169, score-0.863]
83 For [17] and [14], only the running time for their binary version is reported since more time is required for multiple regions cosegmentation (퐾 > 2). [sent-171, score-0.715]
84 It should be noted that the running time shown in this table does not include superpixel extraction and histogram generation steps for all methods. [sent-175, score-0.281]
85 In VOC2012 dataset, only [17] is compared since it can also cosegment images in large scale. [sent-176, score-0.315]
86 For [13] and [14], the requirement on large memory and computation cost for cosegmenting hundreds of images is beyond our computation capability. [sent-177, score-0.316]
87 The cosegmentation accuracy and running time are presented in Table 3 and 4 respectively. [sent-178, score-0.676]
88 For cosegmenting hundreds of images, our method only requires less than one minute, which is much iCoseg VOC201 2 cyaruAc0 0. [sent-180, score-0.276]
89 We also try the cosegmentation experiments at the level of 1000 images. [sent-187, score-0.679]
90 Due to the lack of enough images with groundtruth segmentation in VOC2012 dataset for the accuracy evaluation, we randomly select 1000 related images from its classification challenge set and only test the running time. [sent-188, score-0.236]
91 Our method requires about 5 minutes for cosegmenting 1000 images, while [17] needs 60 − 70 minutes as reported 1in0 0th0e imir paper. [sent-189, score-0.259]
92 Semi-supervised cosegmentation results Next, the cosegmentation experiment is performed in semi-supervised manner (denoted as “SemiSV”) and the result is compared with unsupervised learning (denoted as “UnSV”) as well as supervised learning (denoted as “FullSV”). [sent-193, score-1.435]
93 For supervised learning, each image is segmented individually with training images only, without considering the similarity of the common object shared between unsegmented images. [sent-194, score-0.617]
94 For training image selection in this dataset, we notice that some images have large errors in the superpixel labels, which are determined according to the overlap with the foreground and background pixel labels. [sent-202, score-0.413]
95 That is, the resulting foreground from the converted superpixel labels is significantly different from the original foreground, probably due to bad superpixel extraction. [sent-203, score-0.631]
96 This result shows that semi-supervised learning will be most competent when the number of unsegmented images is far more than that of segmented ones, as concluded in [6]. [sent-207, score-0.482]
97 With the fewest training images in VOC2012 dataset, the accuracy of “FullSV” is close to “UnSV”, which indicates that the similarity information from the common object between test images is competitive to that provided by the segmentation groundtruth of the 4 training images in this dataset. [sent-208, score-0.401]
98 What’s more, the concrete information from the training images may be stained by the uncertainty of the unsegmented images, which worsens the final cosegmentation accuracy. [sent-212, score-1.128]
99 Conclusion In this paper, we proposed a semi-supervised learning method for large scale images cosegmentation, where hundreds of images can be processed in less than one minute with competitive cosegmentation accuracy. [sent-214, score-0.872]
100 Tricos: A tri-level class-discriminative cosegmentation method for image classification. [sent-241, score-0.656]
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