An Efficient Bag-of-Feature Representation for Object Classification

Abstract

The Bag-of-features (BoF) approach has proved to yield better performance in a patch- based object classification system owing to its simplicity. However, often the very large number of patch-based descriptors (such as scale-invariant feature transform and speeded up robust features, extracted from images to create a BoF vector) leads to huge computational cost and an increased storage requirement. This paper demonstrates a two-staged approach to creating a discriminative and compact BoF representation for object classification. In the first stage, ambiguous patch-based descriptors are eliminated using an entropy-based and one-pass feature selection approach, to retain high-quality descriptors in constructing a codebook. In the second stage, a subset of codewords which is not activated enough in images are eliminated from the initially constructed codebook based on statistical measures. Finally, each patch-based descriptor of an image is assigned to the closest codeword to create a histogram representation. One-versus-all support vector machine is applied to classify the histogram representation. The proposed methods are evaluated on benchmark image datasets. Testing results show that the proposed methods enables the codebook to be more discriminative and compact in moderate sized visual object classification tasks.