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159
Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation
- In ICCV
, 2009
"... Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neig ..."
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Cited by 154 (21 self)
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Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing the log-likelihood of the tag predictions in the training set. In this manner, we can optimally combine a collection of image similarity metrics that cover different aspects of image content, such as local shape descriptors, or global color histograms. We also introduce a word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall of rare words. We investigate the performance of different variants of our model and compare to existing work. We present experimental results for three challenging data sets. On all three, TagProp makes a marked improvement as compared to the current state-of-the-art. 1.
Face recognition with learning-based Descriptor
- In Proc. IEEE CVPR
, 2010
"... We present a novel approach to address the representa-tion issue and the matching issue in face recognition (verifi-cation). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Un-like many previous manually designed encoding methods (e.g., LBP or ..."
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Cited by 104 (13 self)
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We present a novel approach to address the representa-tion issue and the matching issue in face recognition (verifi-cation). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Un-like many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning tech-niques to learn an encoder from the training examples, which can automatically achieve very good tradeoff be-tween discriminative power and invariance. Then we ap-ply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further im-prove the discriminative ability of the descriptor. The re-sulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenar-ios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combi-nations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45 % recognition rate), while maintaining excellent compactness, simplicity, and gener-alization ability across different datasets.
H.: Large scale metric learning from equivalence constraints
- In: Proc. IEEE Intern. Conf. on Computer Vision and Pattern Recognition
, 2012
"... In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly ..."
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Cited by 77 (5 self)
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In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. Further, if one considers the constantly growing amount of data it is often infeasible to specify fully supervised labels for all data points. Instead, it is easier to specify labels in form of equivalence constraints. We introduce a simple though effective strategy to learn a distance metric from equivalence constraints, based on a statistical inference perspective. In contrast to existing methods we do not rely on complex optimization problems requiring computationally expensive iterations. Hence, our method is orders of magnitudes faster than comparable methods. Results on a variety of challenging benchmarks with rather diverse nature demonstrate the power of our method. These include faces in unconstrained environments, matching before unseen object instances and person re-identification across spatially disjoint cameras. In the latter two benchmarks we clearly outperform the state-ofthe-art. 1.
An associate-predict model for face recognition
- In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, 2011
"... Handling intra-personal variation is a major challenge in face recognition. It is difficult how to appropriately measure the similarity between human faces under signif-icantly different settings (e.g., pose, illumination, and ex-pression). In this paper, we propose a new model, called “Associate-Pr ..."
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Cited by 56 (7 self)
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Handling intra-personal variation is a major challenge in face recognition. It is difficult how to appropriately measure the similarity between human faces under signif-icantly different settings (e.g., pose, illumination, and ex-pression). In this paper, we propose a new model, called “Associate-Predict ” (AP) model, to address this issue. The associate-predict model is built on an extra generic identity data set, in which each identity contains multiple images with large intra-personal variation. When considering two faces under significantly different settings (e.g., non-frontal and frontal), we first “associate ” one input face with alike identities from the generic identity date set. Using the asso-ciated faces, we generatively “predict ” the appearance of one input face under the setting of another input face, or discriminatively “predict ” the likelihood whether two input faces are from the same person or not. We call the two pro-posed prediction methods as “appearance-prediction ” and “likelihood-prediction”. By leveraging an extra data set (“memory”) and the “associate-predict ” model, the intra-personal variation can be effectively handled. To improve the generalization ability of our model, we further add a switching mechanism- we directly com-pare the appearances of two faces if they have close intra-personal settings; otherwise, we use the associate-predict model for the recognition. Experiments on two public face benchmarks (Multi-PIE and LFW) demonstrated that our final model can substantially improve the performance of most existing face recognition methods
Beyond Simple Features: A Large-Scale Feature Search Approach to Unconstrained Face Recognition
"... Abstract — Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [1], [2]; HOG [3], [4]; or LBP [5], [6]) that transform raw pixel values into a representation better suited to subsequent processing and classification. While the choice of feature ..."
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Cited by 56 (8 self)
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Abstract — Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [1], [2]; HOG [3], [4]; or LBP [5], [6]) that transform raw pixel values into a representation better suited to subsequent processing and classification. While the choice of feature representation is often not central to the logic of a given algorithm, the quality of the feature representation can have critically important implications for performance. Here, we demonstrate a large-scale feature search approach to generating new, more powerful feature representations in which a multitude of complex, nonlinear, multilayer neuromorphic feature representations are randomly generated and screened to find those best suited for the task at hand. In particular, we show that a brute-force search can generate representations that, in combination with standard machine learning blending techniques, achieve state-of-the-art performance on the Labeled Faces in the Wild (LFW) [7] unconstrained face recognition challenge set. These representations outperform previous stateof-the-art approaches, in spite of requiring less training data and using a conceptually simpler machine learning backend. We argue that such large-scale-search-derived feature sets can play a synergistic role with other computer vision approaches by providing a richer base of features with which to work. I.
Relaxed pairwise learned metric for person re-identification
- In ECCV
, 2012
"... Abstract. Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples fro ..."
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Cited by 55 (2 self)
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Abstract. Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting state-of-the-art classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that state-of-the-art results can be obtained at much lower computational costs. 1
Probabilistic models for inference about iden‐ tity
- IEEE TPAMI
, 2012
"... Abstract—Many face recognition algorithms use “distance-based ” methods: feature vectors are extracted from each face and distances in feature space are compared to determine matches. In this paper we argue for a fundamentally different approach. We consider each image as having been generated from ..."
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Cited by 52 (0 self)
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Abstract—Many face recognition algorithms use “distance-based ” methods: feature vectors are extracted from each face and distances in feature space are compared to determine matches. In this paper we argue for a fundamentally different approach. We consider each image as having been generated from several underlying causes, some of which are due to identity (latent identity variables, or LIVs) and some of which are not. In recognition we evaluate the probability that two faces have the same underlying identity cause. We make these ideas concrete by developing a series of novel generative models which incorporate both within-individual and between-individual variation. We consider both the linear case where signal and noise are represented by a subspace, and the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a “tied ” version of the algorithm that allows explicit comparison of faces across quite different viewing conditions. We demonstrate that our model produces results that are comparable or better than the state of the art for both frontal face recognition and face recognition under varying pose.
F.: PCCA: A new approach for distance learning from sparse pairwise constraints
- In: CVPR (2012) Re-identification: What Features Are Important? 401
"... This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high dimensional input space, problem for which most existing distance metric learning approaches are not adapted. PCCA lea ..."
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Cited by 51 (0 self)
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This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high dimensional input space, problem for which most existing distance metric learning approaches are not adapted. PCCA learns a projection into a low-dimensional space where the distance between pairs of data points respects the desired constraints, exhibiting good generalization properties in presence of high dimensional data. The paper also shows how to efficiently kernelize the approach. PCCA is experimentally validated on two challenging vision tasks, face verification and person re-identification, for which we obtain state-of-the-art results. 1.
