Hao Xia , Nanyang Technological University, Singapore
Steven C.H. Hoi , Nanyang Technological University, Singapore
Rong Jin , Michigan State University, East Lansing
Peilin Zhao , Nanyang Technological University, Singapore
Recent years have witnessed a number of studies on distance metric learning to improve visual similarity search in Content-Based Image Retrieval (CBIR). Despite their popularity and success, most existing methods on distance metric learning are limited in two aspects. First, they typically assume the target proximity function follows the family of Mahalanobis distances, which limits their capacity of measuring similarity of complex patterns in real applications. Second, they often cannot effectively handle the similarity measure of multi-modal data that may originate from multiple resources. To overcome these limitations, this paper investigates an online kernel ranking framework for learning kernel-based proximity functions, which goes beyond the conventional linear distance metric learning approaches. Based on the framework, we propose a novel Online Multiple Kernel Ranking (OMKR) method, which learns a flexible nonlinear proximity function with multiple kernels to improve visual similarity search in CBIR. We evaluate the proposed technique for CBIR on a variety of image data sets, in which encouraging results show that OMKR outperforms the state-of-the-art techniques significantly.
Kernel, Measurement, Optimization, Image retrieval, Visualization, Support vector machines, Algorithm design and analysis, Information Technology and Systems, Machine learning, Learning, Artificial Intelligence, Computing Methodologies, Image/video retrieval, Information Search and Retrieval, Information Storage and Retrieval
R. Jin, S. C. Hoi, H. Xia and P. Zhao, "Online Multiple Kernel Similarity Learning for Visual Search," in IEEE Transactions on Pattern Analysis & Machine Intelligence.