Learning visual dictionaries and decision lists for object recognition

Abstract

Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictionaries have been constructed by unsupervised clustering. This paper presents an efficient discriminative approach, called Iterative Discriminative Clustering (IDC), for dictionary learning. In this approach, each dictionary entry is defined by a representative value and a learned distance metric. In IDC algorithm, the dictionary entries are initialized by unsupervised clustering and then locally adapted to improve their discriminative power. Motivated by studies of the characteristics of individual dictionary entries, we employ bagged decision lists (BDL) as our image classifier in order to explore the conjunctions of small number of informative dictionary entries for classification. Experiments on benchmark object recognition datasets show that the system based on the new discriminative dictionaries and BDL classifier give performance comparable or superior to the state-of-art generic object recognition approaches.