Active Visual Learning and Recognition Using Incremental Kernel PCA

Abstract

Eigenspace models are a convenient way to represent set of images with widespread applications. In the traditional approach to calculate these eigenspace models, known as batch PCA method, model must capture all the images needed to build the internal representation. This approach has some drawbacks. Since the entire set of images is necessary, it is impossible to make the model build an internal representation while exploring a new object. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. In this paper we propose a method that allows for incremental eigenspace update method by incremental kernel PCA for vision learning and recognition. Experimental results indicate that accuracy performance of proposed method is comparable to batch KPCA and outperform than APEX. Furthermore proposed method has efficiency in memory requirement compared to KPCA.