Online heterogeneous feature fusion machines for visual recognition

Abstract

Heterogeneous Feature Fusion Machines (HFFM) is a kernel based logistic regression model that effectively fuses multiple features for visual recognition tasks. However, the batch mode solution for HFFM, ‘Block Coordinate Gradient Descent’ (BCGD) has the same low efficiency and poor scalability as the most batch algorithms do. In this paper, we describe a newly developed online learning algorithm in multiple Reproducing Kernel Hilbert Spaces for solving HFFM model. This new algorithm is called OLHFFM, i.e. Online HFFM. OLHFFM is novel combination of kernel-based learning technique with dual averaging gradient descent methods. In addition, group LASSO regularization technique is used in OLHFFM for finding important explanatory coefficients that are related to support samples in group manner. The effectiveness of OLHFFM has been demonstrated by a number of experiments that were conducted on public event, object dataset, as well as on large scale handwritten digital dataset. Using the OLHFFM approach, we have achieved almost equivalent recognition performance to that using batch-mode approach. Experiments conducted on both MIT Caltech-6 and challenging VOC2011 TrainVal object datasets show that OLHFFM is superior in performance to kernel based online learning approaches such as ILK or NORMA. In addition, the classification performance of OLHFFM approach as demonstrated by the experiments conducted on large scale MNIST dataset is comparable to or better than that of the current state-of-the-art online multiple kernel learning approaches such as OM-2, UFO-MKL, OMCL and OMKL. Extensive experiments on visual data classification demonstrate the effectiveness and robustness of the new OLHFFM approach.