Multi-view multi-sparsity kernel reconstruction for multi-class image classification

Abstract

This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

Multi-view Multi-label Learning with Incomplete Views and Labels

“…To process multi-view multi-label data sets, corresponding learning methods including multi-view multi-sparsity kernel reconstruction (MMKR for short) model [13] and multi-feature fusion based on supervised multi-view multilabel canonical correlation projection (sM2CP) [14] are developed. While they have a common issue, namely, how to process data sets with incomplete information, multigranularity label correlation when label-specific features and complementarity information provided.…”

Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

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