iccv iccv2013 iccv2013-75 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jianping Shi, Renjie Liao, Jiaya Jia
Abstract: We propose a co-detection and labeling (CoDeL) framework to identify persons that contain self-consistent appearance in multiple images. Our CoDeL model builds upon the deformable part-based model to detect human hypotheses and exploits cross-image correspondence via a matching classifier. Relying on a Gaussian process, this matching classifier models the similarity of two hypotheses and efficiently captures the relative importance contributed by various visual features, reducing the adverse effect of scattered occlusion. Further, the detector and matching classifier together make our modelfit into a semi-supervised co-training framework, which can get enhanced results with a small amount of labeled training data. Our CoDeL model achieves decent performance on existing and new benchmark datasets.
Reference: text
sentIndex sentText sentNum sentScore
1 Our CoDeL model builds upon the deformable part-based model to detect human hypotheses and exploits cross-image correspondence via a matching classifier. [sent-5, score-0.612]
2 Relying on a Gaussian process, this matching classifier models the similarity of two hypotheses and efficiently captures the relative importance contributed by various visual features, reducing the adverse effect of scattered occlusion. [sent-6, score-0.575]
3 Further, the detector and matching classifier together make our modelfit into a semi-supervised co-training framework, which can get enhanced results with a small amount of labeled training data. [sent-7, score-0.509]
4 Current commercial systems, such as Picasa or facebook, have already provided the human grouping function based on face similarity. [sent-22, score-0.333]
5 Previous human identity grouping research [27, 20, 1] extends faces to torsos, given the fact that a person appearing in multiple images taken in the same day or during the same event often wears the same clothes. [sent-24, score-0.363]
6 Since face detector is vulnerable to headpose variation, not to mention occlusion or back views. [sent-25, score-0.233]
7 In this regard, a reliable human detector would be vastly valuable to the community. [sent-29, score-0.358]
8 For most classical single-image human detectors [5, 29, 6], input images generally contain pedestrians in standing or walking poses. [sent-30, score-0.271]
9 Our method relaxes this latent constraint in detecting and grouping persons, thus working on data that could fail conventional human detectors and human template matching. [sent-31, score-0.57]
10 Moreover, the possible high variation of backgrounds in different images would make the human template matching really challenging. [sent-32, score-0.36]
11 To efficiently utilize the human co-occurrence information in multiple images, we develop a human co-detection 2096 and labeling (CoDeL) framework. [sent-38, score-0.479]
12 It is in a semi-supervised learning manner, since the number of manually annotated regions is limited [8] and most existing human detection datasets [6] seldom offer labeling about whether two detected persons actually correspond to the same one. [sent-39, score-0.425]
13 It trains two classifiers, including a detector and a matching classifier, based on two feature sets, which are conditionally independent given the class labels. [sent-41, score-0.35]
14 In particular, for the detection part, we resort to the deformable part-based model [10, 8], which exploits edge information, like HOG [5], to distinguish human from background. [sent-44, score-0.31]
15 Part-based model represents a human hypothesis as multiple flexible parts. [sent-45, score-0.302]
16 It reduces background noise and is also robust to deformation of human body, which often occurs. [sent-46, score-0.247]
17 For the matching process, we build a potential function through a Gaussian process [15]. [sent-47, score-0.218]
18 It not only incorporates the similarity between any two parts [2] but also measures the similarity between two human hypotheses, thus being robust to partial appearance variation. [sent-48, score-0.273]
19 From another point of view, the two feature sets used by the two classifiers are conditionally independent given human labels. [sent-50, score-0.322]
20 With the initial annotated human regions and labeled matching region pairs, we train the part-based human detector and matching classifier respectively. [sent-51, score-1.095]
21 We regard the positive outputs of one classifier as the weak positive samples of the other, and iterate this process until reliable classifiers are yielded. [sent-52, score-0.396]
22 In testing, given the trained detector and matching classifier, we apply CoDeL to detect and label human regions. [sent-53, score-0.507]
23 Second, we design a new matching classifier to capture occurrence of the same person. [sent-56, score-0.269]
24 Related Work Previous work for human identity grouping [27, 20, 1] usually extracts visual features from face regions and clothes. [sent-60, score-0.362]
25 Performance of the face detector is important in these methods. [sent-61, score-0.233]
26 Another stream of human identity identification [18, 21] is to handle videos via trackers. [sent-63, score-0.273]
27 [9] matches human in crowd images given the user input as initial label to retrieve under a small-motion assumption. [sent-65, score-0.265]
28 It applies a pictorial structure model on human parts (hair, face and torso), which can be regarded as a special version in our general framework. [sent-69, score-0.297]
29 Most previous human detectors concentrate on pedestrians, where sliding windows are adopted. [sent-77, score-0.247]
30 For general human bodies with large deformation, object detector trained on human datasets performs better. [sent-78, score-0.569]
31 Co-Detection and Labeling Given a general human detection training set and an additional small set with matching labels, we aim to build a human co-detection and labeling (CoDeL) solution. [sent-89, score-0.701]
32 , S1]i acned f tahcee technique flloyr detecting faces [24] is mature, we add the face filter f as an additional constraint for human hypothesis. [sent-98, score-0.38]
33 As long as the ratio of overlapping area between human bounding box 2097 and face exceeds a predefined threshold (set to 0. [sent-99, score-0.349]
34 5 in our experiments), face and human are grouped together. [sent-100, score-0.297]
35 Energy Function for CoDeL The goal of our CoDeL model is to incorporate the human detecting and matching classifiers in the same framework, so that the two classifiers could help improve each other by adding weak positive samples according to their classification results. [sent-104, score-0.618]
36 , HramMe}w, aornkd give pair-wise matching scores v Hia =the { matching cla}s,s iafinedr. [sent-113, score-0.336]
37 n+ 1 (1) where Hi is the ith human hypothesis in H. [sent-127, score-0.302]
38 The restriction Hi ∈ In confirms tuhmata nH hi piso dtheetesciste ind Hwi. [sent-128, score-0.236]
39 Hl i∈s t hIe set of all human hypotheses in image Il. [sent-130, score-0.427]
40 (1) is the unary potential term, which measures the compatibility between human hypothesis Hi and observed image In. [sent-132, score-0.488]
41 Em is the matching potential term, measuring pairwise similarity between Hi in image In and human hypotheses set Hl in image Il. [sent-133, score-0.676]
42 Specifically, Hthe unary potential Eu for human hypothesis Hi in image In, which is the detection classifier in our CoDeL model, is defined as Eu(Hi, In) = Ef(fi, In) + Eh(ri, Pi, In), (2) where Ef is the potential which indicates the likelihood of containing a face in the area. [sent-134, score-0.808]
43 ctioexn pd{e−finyed on the )f}ac+e region as gt(hef sum over weighted outputs of weak classifiers, wf is its parameter set, and yi is the classifier label in this region. [sent-137, score-0.239]
44 Eh measures the compatibility between image In and partbased human hypothesis Hi represented by {ri, Pi}. [sent-139, score-0.328]
45 (1) defines the human hypothesis level matching potential between Hi in image In and the set of hypotheses Hl in Il. [sent-152, score-0.736]
46 We model matching as Em(Hi,In,Hl,Il) = T (Hmja∈IxlEˆm(Hi,Hj),t), (4) where T (x, t) is a threshold function to measure the similarity eb Tetw (xe,et)n iHs ia a thndre tshheo lbde sfut nmcatitochne tod h muemaasnur hypothesis Hj in image Il. [sent-154, score-0.323]
47 It can avoid establishing excessive or incorrect matching linkage between any two human pairs. [sent-158, score-0.36]
48 Eˆm (Hi , Hj) reports the similarity between two human hypotheses Hi and Hj . [sent-159, score-0.458]
49 To describe the marginal likeliahroeo mda explicitly, we introduce a latent function and transform the matching value to obtain a valid probability measure as λ p(yij = 1|Hi, Hj ) = σ(λ(Hi, Hj)), (6) where σ is a logistic function. [sent-162, score-0.257]
50 In particular, the input of λ is defined as the difference between two stacked feature vectors extracted from parts of human hypotheses respectively. [sent-164, score-0.456]
51 The correspondences of parts for two human hypotheses are obtained similarly as in the part-base model [10]. [sent-165, score-0.427]
52 (1), the unary potential corresponds to a classifier based on face and part-based human detectors, which largely rely on edge information. [sent-169, score-0.577]
53 The matching potential, differently, contains a classifier taking part-level similarity scores measured as difference upon color and texture features. [sent-170, score-0.405]
54 colortexture) are conditionally independent given human labels, since edges are used to distinguish between human and nonhuman while color and texture are responsible for measuring similarity of two human hypotheses. [sent-172, score-0.785]
55 When the labeled training data are not enough, we can use positive samples produced by one classifier as weak positive ones for updating the other. [sent-174, score-0.364]
56 Model Learning In model learning, given a group of training data with part of them containing detected bounding boxes and a subset with labeled human correspondences, we learn the parameters for Eq. [sent-180, score-0.337]
57 (1), we first train the initial classifiers with labeled training data, and then update the unary and matching terms iteratively in a co-training manner by exploring unlabeled training data. [sent-185, score-0.553]
58 Since frontal faces do not often appear in our dataset, we first obtain the parameter wf in the face detector through training Ada boost [24] on common face dataset and fix it in remaining iterations. [sent-187, score-0.454]
59 Based on the above two updating steps, we perform cotraining to generate new weak labeled positive samples. [sent-212, score-0.221]
60 We first learn two initial classifiers and use the initial trained human detector to test new unlabeled images. [sent-215, score-0.511]
61 Since output of the detector contains no label, they cannot be directly employed by the successive matching classifier. [sent-216, score-0.296]
62 To overcome this problem, we build a confidence criterion based on the probabilistic property of the GP classifier, which lets the mean prediction of the GP learned in last round determine whether two new hypotheses produced by the detector match. [sent-217, score-0.465]
63 The mean prediction of the two human hypotheses Hi∗ and Hj∗ in the 2099 GP classifier is defined as ¯y∗ij=? [sent-218, score-0.547]
64 p(yij|λ∗)p(λ∗|H,Hi∗,Hj∗)dλ∗ (10) where λ∗ is the current latent function corresponding to the test pair and H is the initial training human hypotheses set. [sent-219, score-0.526]
65 two human hypotheses are denoted as weak positive and are added to the training data of matching in the next round. [sent-222, score-0.718]
66 Since these input hypotheses are selected from output of the detector with high unary scores, we pass these hypotheses pairs to retrain the matching classifier, illustrated in Fig. [sent-226, score-0.898]
67 Given the updated matching classifier, we retrieve weak positive human hypotheses to train the detector, shown in the bottom row in Fig. [sent-228, score-0.737]
68 First, a base hypotheses pool is generated by a human detector with a low unary score threshold, thus with high recall. [sent-230, score-0.665]
69 For each pair of hypotheses in this base pool, we calculate the total energy in Eq. [sent-231, score-0.253]
70 Since the total energy indicates the confidence of a human region, we retrain our detector with data remaining in this complete hypothesis pool. [sent-233, score-0.587]
71 Model Inference In model inference, our goal is to detect human hypotheses and report their corresponding labels on new data given the human detector and matching classifier. [sent-239, score-0.934]
72 We use the face and part-based human detectors to find candidates. [sent-240, score-0.333]
73 Then we adopt the GP classifier on each pair of human hypotheses to get the matching score via Eq. [sent-242, score-0.696]
74 If the total score for a particular human body in Eq. [sent-244, score-0.241]
75 Therefore, the final confidence includes both the unary and matching scores. [sent-247, score-0.293]
76 If a human region finds similar ones in other images, which are also labeled as human, it becomes more confident. [sent-248, score-0.27]
77 Meanwhile, the detected false-alarm regions have relatively low unary scores and could hardly find matches among other human regions. [sent-249, score-0.401]
78 human regions and their pairwise matching potential scores, we assign the human labels via hierarchical clustering [22], which assures the difference of scores within a cluster is less than a predefined distance. [sent-256, score-0.678]
79 3) Can our matching classifier correctly distinguish between matched pairs and others? [sent-262, score-0.352]
80 One is the pedestrian dataset provided in [7] where the stereo image pairs serve as a natural source of matched pairs following the setting of [2]. [sent-267, score-0.217]
81 It conforms to the assumptions of our human co-detection tasks that is, each human appears in only part of the image set with consistent appearance. [sent-269, score-0.422]
82 The matching classifier is evaluated by the classification accuracy with respect to ground truth matching labels. [sent-274, score-0.418]
83 In the matching part, we use three sets of features to describe similarity in terms of color and texture as in Eq. [sent-288, score-0.247]
84 Each feature represents a human hypothesis via a stacked vector among all parts. [sent-291, score-0.331]
85 With increase of unlabeled training data, our co-training system gradually enriches the training set by adding weak positive samples and improves the detection performance. [sent-303, score-0.261]
86 (10), which is used to generate weak labeled human pairs to retrain the matching classifier. [sent-307, score-0.563]
87 As the threshold goes up, the error rate drops, pedestrian dataset [7] and our human co-detection (HCD) dataset. [sent-316, score-0.366]
88 Features of matched SIFT yield good results on the pedestrian dataset because the scales of most persons are small and the majority of them are with dark clothes. [sent-324, score-0.24]
89 Other challenges introduced by this dataset include matching pair chosen among all images and background noise caused by deformable body parts. [sent-326, score-0.215]
90 Also the GP classifier is consistently better than the linear SVM classifier used in [2] due to its non-liner property. [sent-329, score-0.24]
91 Co-Detection Results We compare our method with the widely used face detector [24], one of state-of-the-art human detectors [8] and object co-detection method [2]. [sent-332, score-0.48]
92 The face detector cannot deal with the situation that the face is partly or completely missing. [sent-334, score-0.319]
93 We do not evaluate this detector on the pedestrian dataset, since faces can hardly be found. [sent-336, score-0.325]
94 Compared to single-image human detector [8], our matching classifier can increase the score of unreliable human hypotheses when they have confident matches. [sent-337, score-1.054]
95 Further, our CoDeL framework yields a larger increase on the HCD dataset than that on the pedestrian one, since HCD provides more images with potential matching pairs. [sent-341, score-0.321]
96 It is notable that errors may occur when two different human hypotheses are of quite similar appearances, as illustrated in the first row of Fig. [sent-353, score-0.427]
97 Conclusion and Future Work We have proposed a human co-detection and labeling (CoDeL) framework. [sent-360, score-0.268]
98 Also we define our matching classifier via a Gaussian process on the human hypothesis level. [sent-362, score-0.571]
99 Our future work includes extending the matching classifier by integrating spatial relationship among parts. [sent-364, score-0.269]
100 Fast human detection using a cascade of histograms of oriented gradients. [sent-567, score-0.25]
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