Exploring the Power of Kernel in Feature Representation for Object Categorization

摘要

Learning robust and invariant feature representations is always a crucial task in visual recognition and analysis. Mean square error (MSE) has been used in many feature encoding methods as a feature reconstruction criterion. However, due to the non-Gaussian noises and non-linearity structures in natural images, second order statistics like MSE are usually not sufficient to capture these information from image data. In this paper, motivated by the information-theoretic learning framework and kernel machine learning, we adopt a similarity measure called correntropy in the auto-encoder model to tackle this problem. The proposed maximum correntropy auto-encoder (MCAE) learns more robust and discriminative representations than MSE based model by performing computation in an infinite dimensional kernel space. Moreover, we further exploit the power of kernel by learning a kernel embedding neural network which explicitly maps data from Euclidean space to an approximated kernel space. Experimental results on standard object categorization datasets show the effectiveness of kernel learning in feature representation for visual recognition task.