Abstract:
Object recognition is challenging especially when the objects from different categories are visually similar to each other. In this paper, we present a novel joint dictio...Show MoreMetadata
Abstract:
Object recognition is challenging especially when the objects from different categories are visually similar to each other. In this paper, we present a novel joint dictionary learning (JDL) algorithm to exploit the visual correlation within a group of visually similar object categories for dictionary learning where a commonly shared dictionary and multiple category-specific dictionaries are accordingly modeled. To enhance the discrimination of the dictionaries, the dictionary learning problem is formulated as a joint optimization by adding a discriminative term on the principle of the Fisher discrimination criterion. As well as presenting the JDL model, a classification scheme is developed to better take advantage of the multiple dictionaries that have been trained. The effectiveness of the proposed algorithm has been evaluated on popular visual benchmarks.
Date of Conference: 16-21 June 2012
Date Added to IEEE Xplore: 26 July 2012
ISBN Information: