Abstract:
In this paper, motivated by the superior performance of sparse representation based dictionary learning for application of image classification and the usage of nonlinear...Show MoreMetadata
Abstract:
In this paper, motivated by the superior performance of sparse representation based dictionary learning for application of image classification and the usage of nonlinearity property in improving performance of image representation, we propose a locality sensitive dictionary learning algorithm with global consistency and smoothness constraint to overcome the restriction of linearity at relatively low cost. Specifically, the image features are partitioned into several groups in a locality sensitive way and a global consistency regularizer is embedded into locality sensitive dictionary learning algorithm. The proposed algorithm is efficient to capture complex nonlinear structure. Experimental results on several benchmark data sets demonstrate the efficiency of our proposed locality sensitive dictionary learning algorithm.
Date of Conference: 27-30 September 2015
Date Added to IEEE Xplore: 10 December 2015
ISBN Information: