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Kernel Methods for Multi-Label Learning and Object Categorization

We are interested in the problem of multi-label learning with many classes, where the aim is assigning each instance to related categories (Figure 1). Our goal is to develop kernel methods for multi-label learning. Unlike the conventional approaches that implement multi-label learning as a set of binary classification problems, our goal is to develop direct approaches that would exploit relationships between the class labels, and we proposed multi-label ranking methods for this task (see references below). Moreover, since kernel learning, or more specifically Multiple Kernel Learning (MKL), has attracted considerable amount of interest in computer vision community, we proposed an efficient algorithm for multi-label multiple kernel learning (ML-MKL) see reference below. We assume that all the classes under consideration share the same combination of kernel functions, and the objective is to find the optimal kernel combination that benefits all the classes.

results
Fig. 1: For the four images from the VOC 2007 dataset, the original labels are given in addition to the outputs of the proposed method.

We also consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c1, c2, c3) is only assigned to class c1 when it is used as a training sample. We refer to this problem as multi-label learning with incomplete class assignment (Figure 2). We proposed a ranking based multi-label learning framework (MLR-GL) that explicitly addresses the challenge of learning from incompletely labeled data by exploiting the group lasso technique to combine the ranking errors.

results
Fig. 2: Examples of training images from the ESP Game dataset with true labels and annotations generated by different multi-label learning methods. Only the underlined true labels are provided to the methods for training. For the proposed method [MLR-GL], the correct (returned) keywords are highlighted by bold font whereas the incorrect ones are highlighted by italic font.

S. S. Bucak, R. Jin, and A. K. Jain, "Multi-label Learning with Incomplete Class Assignments," IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, CO, 2011.

S. S. Bucak, R. Jin, and A.K. Jain, "Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition," NIPS 2010, Vancouver, B.C., Canada, 2010.

S. S. Bucak, P. K. Mallapragada, R. Jin, and A. K. Jain, "Efficient Multi-label Ranking for Multi-class Learning: Application to Object Recognition," International conference on Computer Vision (ICCV 2009), Kyoto, Japan, 2009.

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