Abstract:
In a patch-based object classification system, one of the most popular image representation approach is the bag-of-features (BoF) representation. However, the number of patch-based features extracted from images to create the BoF vector is usually very large, which causes two problems: (i) Increased amount of computational needs during the vector quantisation step, and (ii) Ambiguous descriptors from training images increase false positive rate in the classification. To overcome these issues we introduce a one-pass feature selection approach followed by an entropy-based filtering technique to eliminate the ambiguous features from initial large feature set. In this work, a discriminative BoF representation for object recognition is constructed using patch-based descriptors that are informative in distinguishing object categories. Following the construction of a codebook a subset of codewords which is not activated enough in images is eliminated based on statistical measures and visual-bit representation of codewords. The proposed technique is evaluated on (i) Xerox7, (ii) UIUCTex, (iii) PASCAL VOC 2007, and (iv) Caltech101 image datasets. The proposed feature selection step increases the discriminant power of a codebook, while the codeword selection method maintains the codebook to be more compact. The proposed framework would help to optimise the BoF representation to be effective with stable performance.
