Applying feature selection techniques for visual dictionary creation in object classification

This paper introduces improved methods for visual dictionary creation in an object classification system. In literature, the visual dictionary is often created from a large candidate set of features by random selection or by a clustering algorithm. We apply techniques from feature selection literature to create a more optimal visual dictionary and contribute with a novel feature selection algorithm. As a second step, feature extraction techniques for creating the candidate set are investigated. Subsequently, the size of the candidate set is varied. It was found that the exploitation of feature selection techniques gives a clear improvement of 2-5% in classification rate at no additional computational cost in normal system operation. The proposed algorithm called extremal optimization, outperforms state-of-the-art algorithms. The paper discloses results on candidate set creation using interest point operators. As a general bonus, the evaluated feature selection techniques are generally applicable to any problem that uses a dictionary of features, as typically applied in the object recognition domain.