Abstract—Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear subspace learning can sufficiently boost the discriminating power, as measured by discriminating power coefficient (DPC). The proposed solution achieves good classification accuracy gains and shows computationally efficient. Particularly, we approximate the global nonlinearity through a multimodal localized piecewise subspace learning framework, in which three locality criteria can work individually or jointly for any new subspace learning algorithm design. It turns out that most existing subspace learning methods can be unified in such a common framework embodying either the global or local learning manner. On the other hand,

We investigate the problem of combining multiple feature channels for the purpose of efficient image classification. Discriminative kernel based methods, such as SVMs, have been shown to be quite effective for image classification. To use these methods with several feature channels, one needs to combine base kernels computed from them. Multiple kernel learning is an effective method for combining the base kernels. However, the cost of computing the kernel similarities of a test image with each of the support vectors for all feature channels is extremely high. We propose an alternate method, where training data instances are selected for each of the base kernels using boosting. A composite decision function is learnt, which can be evaluated by computing kernel similarities with respect to only these chosen instances. This method significantly reduces the number of kernel computations required during testing. Experimental results on the benchmark UCI datasets, as well as on two challenging painting and chart datasets, are included to demonstrate the effectiveness of our method.

In this paper a novel ranking-based face recognition (FR) scheme is proposed. Compared with classical twoclass (intra/extra person) and multi-class (each person a single class) schemes, the ranking-based method only takes into account the most relevant information in training data to find a solution, and therefore is more consistent with the objective of FR. In our approach, given a feature set and its similarity measure, all interested image pairs will be ordered by similarity. The solution to FR then becomes to explore a ranking function that can rank each intra-personal similarity prior to its relevant extra-personal similarities, which can be readily solved by RankBoost algorithm. Furthermore in this paper, a Logit-RankBoost algorithm is proposed which can achieve better recognition performance, and a pruning technique is adopted to deal with the large amount of data that results in further improvement in recognition accuracy. Extensive experimental results on a consumer image collection and the FERET dataset are reported to show the effectiveness of our approach. 1.

In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object detection. Confidence rated Ada-boost algorithm has not been applied to generic object detection problem; in that sense our work is novel. We represent images as bag of words, where the words are SIFT descriptors extracted over some interest points. We compare our boosting algorithm to another version of boosting algorithm called Gentle-boost. Our approach generalizes well and performs equal or better than Gentle-boost. We show our results on four categories from the Caltech data sets, in terms of ROC curves. 1.