Image Categorization by Learned PCA Subspace of Combined Visual-words and Low-level Features

Image category recognition is important to access visual information on the level of objects and scene types. This paper combines different feature representations of images and learn a compact subspace of different features for the automatic recognition of object and scene classes. Compact visual-words and low-level-features object class subspaces are automatically learned from a set of training images by a Principle Component Analysis (PCA) algorithm, and the extracted PCA-domain features are used for Support Vector Machine (SVM) classifier. The main contribution of this paper is two fold: i) Different features (bag-of-features and low-level features)is fused for image representation. ii) The compact feature subspaces (low-dimension features) of different features are learned for rendering to SVM classifier, which is computationally efficient for image category. High classification accuracy is demonstrated on object recognition database (Caltech). We confirm that the proposed strategy is comparable with state-of-the-art methods for object recognition databases.