Multiple kernel-based multi-instance learning algorithm for image classification

作者：Li Daxiang^*; Wang Jing; Zhao Xiaoqiang; Liu Ying; Wang Dianwei

来源：Journal of Visual Communication and Image Representation, 2014, 25(5): 1112-1117.

DOI：10.1016/j.jvcir.2014.03.011

• Multi-instance learning (MIL)
• Image classification
• Affinity propagation (AP)
• Multiple kernel learning (MKL)
• Image retrieval
• Visual words
• Cluster analysis
• Support vector machines

In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a "word-space" from instances based on a collection of "visual-words" generated by affinity propagation (AP) clustering method. After calculating the distance between a "visual-word" and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the "word-space". In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.

• 出版日期2014-7
• 单位西安邮电大学