Multi-View Visual Recognition of Imperfect Testing Data

A practical yet under-explored problem often encountered by multimedia researchers is the recognition of imperfect testing data, where multiple sensing channels are deployed but interference or transmission distortion corrupts some of them. Typical cases of imperfect testing data include missing features and feature misalignments. To address these challenges, we choose the latent space model and introduce a new similarity learning canonical-correlation analysis (SLCCA) method to capture the semantic consensus between views. The consensus information is preserved by projection matrices learned with modified canonical-correlation analysis (CCA) optimization terms with new, explicit classsimilarity constraints. To make it computationally tractable, we propose to combine a practical relaxation and an alternating scheme to solve the optimization problem. Experiments on four challenging multi-view visual recognition datasets demonstrate the efficacy of the proposed method. Categories and S...

• Type: Journal
• Year: 2015
• Where: MM
• Authors: Qilin Zhang, Gang Hua
• Keywords: MM 2015, Multimedia