iccv iccv2013 iccv2013-6 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, S. Shankar Sasrty
Abstract: In many image/video/web classification problems, we have access to a large number of unlabeled samples. However, it is typically expensive and time consuming to obtain labels for the samples. Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classification performance. Most existing active learning algorithms select only one sample at a time prior to retraining the classifier. Hence, they are computationally expensive and cannot take advantage of parallel labeling systems such as Mechanical Turk. On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant information overlap or they involve solving a non-convex optimization. More importantly, the majority of active learning algorithms are developed for a certain classifier type such as SVM. In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. Unlike the state of the art, our algorithm can be used in conjunction with any type of classifiers, including those of the fam- ily of the recently proposed Sparse Representation-based Classification (SRC). We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples, which have the least information overlap. Our method can incorporate the data distribution in the selection process by using the appropriate dissimilarity between pairs of samples. We show the effectiveness of our framework in person detection, scene categorization and face recognition on real-world datasets.
Reference: text
sentIndex sentText sentNum sentScore
1 Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classification performance. [sent-4, score-0.725]
2 Most existing active learning algorithms select only one sample at a time prior to retraining the classifier. [sent-5, score-0.685]
3 On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant information overlap or they involve solving a non-convex optimization. [sent-7, score-0.893]
4 More importantly, the majority of active learning algorithms are developed for a certain classifier type such as SVM. [sent-8, score-0.621]
5 In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. [sent-9, score-0.879]
6 We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples, which have the least information overlap. [sent-11, score-1.15]
7 Active learning is the problem of progressively selecting and annotating the most informative data points from the pool of unlabeled samples, in order to obtain a high classification performance. [sent-22, score-0.797]
8 The majority of the literature consider the single mode active learning [21, 23, 25, 27, 29, 3 1], where the algorithm selects and annotates only one unlabeled sample at a time prior to retraining the classifier. [sent-25, score-1.197]
9 Third, single mode active learning schemes might select and annotate an outlier instead of an informative sample for classification [26]. [sent-29, score-0.91]
10 To address some of the above issues, more recent methods have focused on the batch mode active learning, where they select and annotate multiple unlabeled samples at a time prior to retraining the classifier [2, 5, 12, 17, 18]. [sent-31, score-1.618]
11 No- tice that one can run a single mode active learning method multiple times without retraining the classifier in order to select multiple unlabeled samples. [sent-32, score-1.291]
12 We demonstrate the effectiveness of our proposed active learning framework on three problems of person detection, scene categorization and face recognition. [sent-34, score-0.586]
13 the classification performance compared to the single mode active learning scheme. [sent-41, score-0.608]
14 Other approaches try to decrease the information overlap among the selected unlabeled samples [2, 12, 13, 18, 36]. [sent-42, score-0.791]
15 Moreover, similar to the single mode active learning, most batch mode active learning algorithms are developed for a certain type of a classifier and cannot be easily modified to work with other classifier types [12, 13, 17, 29, 32, 34]. [sent-44, score-1.513]
16 In this paper, we develop an efficient active learning framework based on convex program- ming that can be used in conjunction with any type of classifiers. [sent-46, score-0.577]
17 We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples. [sent-47, score-1.15]
18 More specifically, for each unlabeled sample, we define a confidence score that reflects how uncertain the sample’s predicted label is according to the current classifier and how dissimilar the sample is with respect to the labeled training samples. [sent-48, score-1.326]
19 A large value of the confidence score for an unlabeled sample means that the current classifier is more certain about the predicted label of the sample and also the sample is more similar to the labeled training samples. [sent-49, score-1.401]
20 On the other hand, an unlabeled sample with a small confidence score is more informative and should be labeled. [sent-51, score-0.929]
21 Since we can have many unlabeled samples with low confidence scores and they may have information overlap with each other, i. [sent-52, score-1.121]
22 , can be similar to each other, we need to select a few representatives of the unlabeled samples with low confidence scores. [sent-54, score-1.201]
23 The algorithm that we develop has the following advantages with respect to the state of the art: It addresses the batch mode active leaning problem, hence, it can take advantage of parallel annotation systems such as Mechanical Turk and LabelMe. [sent-56, score-0.586]
24 The choice of the classifier affects selection of unlabeled samples through the confidence scores, but the proposed framework is generic. [sent-58, score-1.184]
25 Unlike the state of the art, it incorporates both the classifier uncertainty and sample diversity in a convex optimization to select multiple informative samples that are diverse with respect to each other and the labeled samples. [sent-62, score-1.287]
26 Active Learning via Convex Programming In this section, we propose an efficient algorithm for active learning that takes advantage of convex programming in order to find the most informative points. [sent-140, score-0.644]
27 To do so, we use the two principles of classifier uncertainty and sample diversity to define confidence scores for unlabeled samples. [sent-142, score-1.314]
28 A lower confidence score for an unlabeled sample indicates that we can obtain more information by annotating that sample. [sent-143, score-0.862]
29 However, the number of unlabeled samples with low confidence scores can be large and, more importantly, the samples can have information overlap with each other or they can be outliers. [sent-144, score-1.453]
30 Thus, we integrate the confidence scores in the DSMRS framework in order to find a few representative unlabeled samples that have low confidence scores. [sent-145, score-1.403]
31 In the subsequent sections, we define the confidence scores and show how to use them in the DSMRS framework in order to find the most informative samples. [sent-146, score-0.489]
32 Classifier Uncertainty First, we use the classifier uncertainty in order to select informative points for improving the classifier performance. [sent-150, score-0.636]
33 The uncertainty sampling principle [4] states that the informative samples for classification are the ones that the classifier is most uncertain about. [sent-151, score-0.827]
34 Notice that the classifier is more confident about the labels of samples in and as they are farther from the decision boundary, Gj(i) G3(1) G2(2) G2(1), while it is less confident about the labels of samples in swihniclee they are ccolnosfiedre ntot atbheo hyperplane boundary. [sent-159, score-0.955]
35 Inn G this case, labeling any of the samples in or does not change the decision boundary, hence, samples in will scthial n bgee m thiesc dleascsiisfiioend. [sent-160, score-0.798]
36 Now, for a generic classifier, we define its confidence about the predicted label of an unlabeled sample. [sent-162, score-0.647]
37 We define the classifier confidence score of point i as cclassifier(i) ? [sent-171, score-0.489]
38 where xij denotes the representation coefficients using labeled samples from class j. [sent-194, score-0.538]
39 Noatned dth Gat the most uncertain samples according to the classifier are samples from G1(1) and G(12), which are close to the decision boundary. [sent-201, score-0.971]
40 Hareow saevmeprl, slab freolimng G sucha samples does not change the decision boundary G2(1) G2(2) much and samples in and will still be misclassified. [sent-202, score-0.768]
41 Right: mlaubeclhing an samples ethsa int are sufafincdien Gtly dissimilar from the labeled training samples helps to improve the classification performance. [sent-203, score-1.0]
42 Sample Diversity We also use the sample diversity criterion in order to find the most informative points for improving the classifier performance. [sent-212, score-0.611]
43 More specifically, sample diversity states that informative points for classification are the ones that are sufficiently dissimilar from the labeled training samples (and from themselves in the batch mode setting). [sent-213, score-1.337]
44 The max-margin hyperplane learned via SVM for the two training samples is shown in the the left plot of Figure 3. [sent-219, score-0.481]
45 le Ndo samples t( shaamvep slmesa ilnl E Guclidaenadn G Gdistances to the labeled samples in this example). [sent-221, score-0.81]
46 In fact, labeling any of the samples in or does not change the decision boundary much, and the points in will be still mdeicsicsliaosnsif bioedun as belonging tod chleas pso i2n. [sent-222, score-0.536]
47 O inn Gthe other hand, samples in and are more dissimilar from the labeled training samples. [sent-223, score-0.61]
48 In fact, labeling a sample from or changes the decision boundary so that points in the Gj(i) G1(1) G1(1) G2(1) G2(2) G1(2) G1(2) G2(1) G2(1) G2(2) 212 linear SVM learned using two training samples (green crosses). [sent-224, score-0.695]
49 Middle: two samples with lowest confidence scores correspond to two samples from that are close to the decision boundary. [sent-227, score-1.093]
50 A retrained classifier using samples, which have information overlap, still misclassifies samples Right: two representatives to a sample from G2(1) and a sample from G(22). [sent-228, score-1.006]
51 A retrained these two of samples with low confidence scores correspond classifier using these two samples correctly classifies all the samples in the dataset. [sent-229, score-1.632]
52 To incorporate diversity with respect to the labeled training set, L, for a point iin the unlabeled set, U, we define tinheg diversity confidence score as cdiversity(i) ? [sent-231, score-1.237]
53 When the closest labeled sample to an unlabeled sample iis very similar to it, i. [sent-233, score-0.787]
54 On the other hand, when all labeled samples are very dissimilar from an unlabeled sample i, i. [sent-239, score-1.058]
55 This means that selecting and annotating sample ipromotes diversity with respect to the labeled samples. [sent-244, score-0.53]
56 Selecting Informative Samples Recall that our goal is to have a batch mode active learn- ing framework that selects multiple informative and diverse unlabeled samples, with respect to the labeled samples as well as each other, for annotation. [sent-247, score-1.661]
57 One can think of a simple algorithm that selects samples that have the lowest confidence scores. [sent-248, score-0.672]
58 The drawback of this approach is that while the selected unlabeled samples are diverse with respect to the labeled training samples, they can still have significant information overlap with each other. [sent-249, score-1.01]
59 This comes from the fact that the confidence scores only reflect the relationship of each unlabeled sample with respect to the classifier and the labeled training samples and do not capture the relationships among the unlabeled samples. [sent-250, score-1.927]
60 A max-margin hyperplane learned via SVM for the two training samples is shown in the the left plot of Figure 4. [sent-253, score-0.481]
61 No- G2(2) some samples in G2(1) tice that samples in G2(1) have small classifier and diversity confidence scores and samples in G2(2) have small diversity ccoonnffiiddeennccee scores. [sent-255, score-1.909]
62 aNndow sa, imf we ss ienlec Gt two samples with lowest confidence scores, we will select two as they are very oclreosse, wtoe twhiel d seecliescito tnw boundary. [sent-256, score-0.666]
63 In fact, after adding these two samples to the labeled training set, the retrained classifier, shown in the middle plot of Figure 4, still misclassifies sam- samples from G(21), ples in G2(2). [sent-258, score-1.014]
64 × On the other hand, two representatives of samples iwni tGh low confidence scores, i. [sent-259, score-0.764]
65 , two samples that capture the distribution of samples with low confidence scores, correspond to one sample from and one sample from . [sent-261, score-1.168]
66 To select a few diverse representatives of unlabeled samples that have low confidence scores, we take advantage of the DSMRS algorithm. [sent-263, score-1.231]
67 Let D ∈ R|U| |U| be the dissimilarity MmRaStrix a gfoorr samples i nD th ∈e R unlabeled set U = s{iim1 , ·l a· r·i , i|U| }at. [sent-264, score-0.792]
68 , c(i|U| i)x) i Zs th ∈e c Ronfidence matrix with the active learning confidence scores, c(i), defined as c(ik) s. [sent-274, score-0.691]
69 (9) More specifically, for an unlabeled sample ik that has a small confidence score c(ik), the optimization program puts less penalty on the k-th row of Z being nonzero. [sent-278, score-0.966]
70 On the other hand, for a sample ik that has a large confidence score c(ik), the optimization program puts more penalty on the k-th row of Z being nonzero. [sent-279, score-0.591]
71 Classification accuracy of different active learning algorithms on the INRIA Person dataset as a function of the total number of labeled training samples selected by each algorithm. [sent-281, score-1.006]
72 motes selecting a few unlabeled samples with low confidence scores that are, at the same time, representatives of the distribution of the samples. [sent-282, score-1.277]
73 To illustrate the effect of confidence scores and representativeness of samples in the performance of our proposed framework, we consider several methods for comparison. [sent-297, score-0.724]
74 We select Kt samples uniformly at random from the pool of unlabeled samples. [sent-301, score-0.811]
75 We select Kt samples that have the smallest classifier confidence scores. [sent-303, score-0.841]
76 Total number of samples from each class of INRIA Person dataset selected by our proposed algorithm (CPAL) at different active learning iterations. [sent-306, score-0.846]
77 We use the positive/negative training images in the dataset to form the pool of unlabeled samples (2, 416 positive and 2, 736 negative samples) and use the the positive/negative test images for testing (1, 126 positive and 900 negative samples). [sent-313, score-0.792]
78 Figure 5 shows the classification accuracy of different active learning methods on the test set as a function of the total number of labeled samples. [sent-317, score-0.654]
79 This comes from the fact that the selected samples by CCAL can have information overlap and are not necessarily representing the distribution of unlabeled samples with – – 214 Figure 7. [sent-321, score-1.123]
80 Classification accuracy of different active learning algorithms on the Fifteen Scene Categories dataset as a function of the total number of labeled training samples selected by each algorithm. [sent-322, score-1.006]
81 Although our active learning algorithm is unaware of the separation of unlabeled samples into classes, it consistently selects about the same number of samples from each class. [sent-326, score-1.526]
82 We randomly select 80% of images in each class to form the pool of unlabeled samples and use the rest of the 20% of images in each class for testing. [sent-333, score-0.931]
83 Figure 7 shows the accuracy of different active learning methods on the test set as a function of the total number of selected samples. [sent-338, score-0.485]
84 Classification accuracy of different active learning algorithms on the Extended YaleB Face dataset as a function of the total number of labeled training samples selected by each algorithm. [sent-341, score-1.006]
85 vious section, here the RAND method, in general, has a better performance than CCAL method that selects multiple samples with low confidence scores. [sent-342, score-0.672]
86 A careful look into the selected samples by different methods shows that, this is due to the fact that CCAL may repeatedly select similar samples from a fixed class while a random strategy, in general, does not get stuck to repeatedly select similar samples from a fixed class. [sent-343, score-1.215]
87 We randomly select 80% of images in each class to form the pool of unlabeled samples and use the rest of the 20% of images in each class for testing. [sent-349, score-0.931]
88 To the best of our knowledge, our work is the first one addressing the active learning problem in conjunction with SRC. [sent-351, score-0.484]
89 Figure 8 shows the classification accuracy of different active learning methods as a function of the total number of labeled training samples selected by each algorithm. [sent-354, score-1.064]
90 With a total of 790 labeled samples (average of 21 samples per class), we obtain the same accuracy (about 97%) as reported in [35] for 32 random samples per class. [sent-356, score-1.173]
91 As 215 a result, samples with low confidence scores are generally dissimilar from each other. [sent-360, score-0.786]
92 Conclusions We proposed a batch mode active learning algorithm based on simultaneous sparse recovery that can be used in conjunction with any classifier type. [sent-362, score-0.924]
93 The advantage of our algorithm with respect to the state of the art is that it incorporates classifier uncertainty and sample diversity principles via confidence scores in a convex programming scheme. [sent-363, score-1.072]
94 Thus, it selects the most informative unlabeled samples for classification that are sufficiently dissimilar from each other as well as the labeled samples and represent the distribution of the unlabeled samples. [sent-364, score-1.899]
95 Incorporating diversity in active learning with support vector machines. [sent-382, score-0.585]
96 Semi-supervised svm batch mode active learning with applications to image retrieval. [sent-456, score-0.699]
97 Text classification from labeled and unlabeled documents using em. [sent-511, score-0.626]
98 An analysis of active learning strategies for sequence labeling tasks. [sent-544, score-0.489]
99 Beyond active noun tagging: Modeling contextual interactions for multi-class active learning. [sent-549, score-0.706]
100 Incorporating diversity and density in active learning for relevance feedback. [sent-602, score-0.585]
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