Multiple kernel learning with ICA: Local discriminative image descriptors for recognition

Local image features have been proven to be a powerful way to describe pattern of interest, both from single objects and complex scenes. While learning from images represented by local features is challenging, recent publications and developments in object recognition has shown that significant performance achievements can be achieved by carefully combining multi-level, coarse-to-fine, sparsely distributed feature encodings, and kernel based learning methods, which defines a generalized similarity measure among data using multiple kernel functions instead of a single one, also known as multiple kernel learning (MKL). In this paper we show that the Kernel ICA descriptors based MKL supervised learning approach perform better than other descriptors for object recognition, since the ICA-based representation is localized. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, MKL classifies the ICA features as discriminative components. We demonstrate our algorithm on different databases for recognition tasks, showing that the proposed method is accurate and more efficient than current approaches.