<title>Generic object recognition by combining distinct features in machine learning</title>

Abstract

Generic object detection and recognition systems need to be able to recognize objects even if they occur at arbitrary scales, or shown from different perspectives on highly textured backgrounds. This problem has recently gained a lot of attention in the field of computer vision e.g. Agarwal and Roth [1], Fergus et al. [2] and Opelt et al. [3]. We propose several modifications to the framework of generic object recognition system as described in [3]. At first, we use K-means to cluster the features into a uniform frame in order to obtain a simple feature vector per image. Secondly, we hypothesis that by combining the distinct features using Kernel Canonical Correlation Analysis (KCCA) we would be able to increase the classification power (Vinokourov et al. [4]). Finally, we use a Support Vector Machine (SVM) classifier in the semantic space obtained by KCCA. In our experiments we compare our method to SVM on the raw data and to the results published in [2, 3]. We are able to show that our proposed approach is able to achieve improved performance on both simple [2,3] and difficult [3] datasets. And the overall complexity of our system is significantly lower than that in [3].