Multi-class active learning for image classification

Joshi, A.J.; Porikli, Fatih; Papanikolopoulos, N.

doi:10.1109/cvprw.2009.5206627

Cited by 112 publications

(113 citation statements)

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“…For multi-class problems, a concept referred to as "classifiers in contention" (the classifiers most likely to be affected by choosing an example for active learning) is introduced in [15]. This concept can also be employed for forming an interference measure -if two examples are likely to affect two different classifiers, they likely carry different information and are not redundant.…”

Section: Classifiers In Contentionmentioning

confidence: 99%

“…We employ the 2-step approach proposed by Joshi et al [15]. In the first step, binary probability values of class membership are estimated for all the binary subproblems.…”

Section: Probability Estimatesmentioning

confidence: 99%

“…The method of pairwise coupling [11], [27] is used to couple the binary estimates and infer a multi-class membership distribution. Such an estimation approach is computationally efficient and generalizes well to problems having many categories [15]. For implementation, the LIBSVM [5] toolbox is employed.…”

Section: Probability Estimatesmentioning

confidence: 99%

“…We choose the probabilistic measure proposed in [15], and shown to be effective for problems having a large number of categories. The selection measure is the difference between the probability values of the top two classes obtained from the estimated distribution, or (c 1 (P ) − c 2 (P )) in the above notation.…”

Section: Utility Measurementioning

confidence: 99%

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Multi-class batch-mode active learning for image classification

Joshiy

Porikli

Papanikolopoulos

2010

2010 IEEE International Conference on Robotics and Automation

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Accurate image classification is crucial in many robotics and surveillance applications -for example, a vision system on a robot needs to accurately recognize the objects seen by its camera. Object recognition systems typically need a large amount of training data for satisfactory performance. The problem is particularly acute when many object categories are present. In this paper we present a batch-mode active learning framework for multi-class image classification systems. In active learning, images are to be chosen for interactive labeling, instead of passively accepting training data. Our framework addresses two important issues: i) it handles redundancy between images which is crucial when batch-mode selection is performed; and ii) we pose batch selection as a submodular function optimization problem that makes an inherently intractable problem efficient to solve, while having approximation guarantees. We show results on image classification data in which our approach substantially reduces the amount of training required over the baseline. ICRA 2010This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-Accurate image classification is crucial in many robotics and surveillance applications -for example, a vision system on a robot needs to accurately recognize the objects seen by its camera. Object recognition systems typically need a large amount of training data for satisfactory performance. The problem is particularly acute when many object categories are present. In this paper we present a batch-mode active learning framework for multi-class image classification systems. In active learning, images are to be chosen for interactive labeling, instead of passively accepting training data. Our framework addresses two important issues: i) it handles redundancy between different images which is crucial when batch-mode selection is performed; and ii) we pose batchselection as a submodular function optimization problem that makes an inherently intractable problem efficient to solve, while having approximation guarantees. We show results on image classification data in which our approach substantially reduces the amount of training required over the baseline.

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Section: Classifiers In Contentionmentioning

confidence: 99%

“…We employ the 2-step approach proposed by Joshi et al [15]. In the first step, binary probability values of class membership are estimated for all the binary subproblems.…”

Section: Probability Estimatesmentioning

confidence: 99%

Section: Probability Estimatesmentioning

confidence: 99%

Section: Utility Measurementioning

confidence: 99%

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Multi-class batch-mode active learning for image classification

Joshiy

Porikli

Papanikolopoulos

2010

2010 IEEE International Conference on Robotics and Automation

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“…P (label = M L) and P (label = CL) are approximated as the proportion of pairs in same and different clusters. Similar in spirit to active learning for training classifiers [36], we select the pair with the highest entropy under the distribution P (label | d ij ) and present it to the user to solicit label.…”

Section: Active Selection Of Pairmentioning

confidence: 99%

Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations

Lad

Parikh

2014

Computer Vision – ECCV 2014

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(ABSTRACT)Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user preferences. Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and which ones do not (cannot-link). These approaches require many such constraints before achieving good clustering performance because each constraint only provides weak cues about the desired clustering.In this work, we propose to use image attributes as a modality for the user to provide more informative cues. In particular, the clustering algorithm iteratively and actively queries a user with an image pair. Instead of the user simply providing a must-link/cannot-link constraint for the pair, the user also provides an attribute-based reasoning e.g. "these two images are similar because both are natural and have still water" or "these two people are dissimilar because one is way older than the other". Under the guidance of this explanation, and equipped with attribute predictors, many additional constraints are automatically generated.We demonstrate the effectiveness of our approach by incorporating the proposed attributebased explanations in three standard semi-supervised clustering algorithms: Constrained K-Means, MPCK-Means, and Spectral Clustering, on three domains: scenes, shoes, and faces, using both binary and relative attributes.

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