Sparsification of Graph Laplacian for Image Indexing using Multidimensional Spectral Hashing
for smaller neighborhoods. But in the most of the cases, multimedia applications requires an algorithm for larger neighborhoods. This creates a research potential to develop a novel approach for generating optimal binary code to retrieve larger neighborhoods. This paper proposes multidimensional spectral hashing that uses sparsification of graph laplacian. Multidimensional spectral hashing uses outer product Eigen functions to improve the codes. The exponential growth of outer product functions are handled using kernel-trick. This makes our proposed algorithm to achieve storage-efficient multi- dimensional spectral hashing. The performance analysis of our proposed algorithm shows better result in terms of binary code generation, true
positives and retrieving neighbor’s images.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.