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.
|Number of pages
|Journal of Visual Communication and Image Representation
|Early online date
|3 Apr 2014
|Published - 1 Jul 2014