Object localization and classification are important problems incomputer vision. However, in many applications, exhaustive searchover all class labels and image locations is computationallyprohibitive. While several methods have been proposed to makeeither classification or localization more efficient, few havedealt with both tasks simultaneously. This paper proposes anefficient method for concurrent object localization andclassification based on a data-dependent multi-classbranch-and-bound formalism. Existing bag-of-featuresclassification schemes, which can be expressed as weightedcombinations of feature counts can be readily adapted to ourmethod. We present experimental results that demonstrate the meritof our algorithm in terms of classification accuracy, localizationaccuracy, and speed, compared to baseline approaches includingexhaustive search, the ISM method, and single-class branch andbound.
