Supervised Visual Vocabulary with Category Information

Abstract

The bag-of-words model has been widely employed in image classification and object detection tasks. The performance of bag-of-words methods depends fundamentally on the visual vocabulary that is applied to quantize the image features into visual words. Traditional vocabulary construction methods (e.g. k-means) are unable to capture the semantic relationship between image features. In order to increase the discriminative power of the visual vocabulary, this paper proposes a technique to construct a supervised visual vocabulary by jointly considering image features and their class labels. The method uses a novel cost function in which a simple and effective dissimilarity measure is adopted to deal with category information. And, we adopt a prototype-based approach which tries to find prototypes for clusters instead of using the means in k-means algorithm. The proposed method works as the k-means algorithm by efficiently minimizing a clustering cost function. The experiments on different datasets show that the proposed vocabulary construction method is effective for image classification.