Feature Combination in Kernel Space for Distance Based Image Hashing

Abstract

The paper presents a novel feature based indexing scheme for image collections. The scheme presents the extension of distance based hashing to kernel space for generating the indexing structure based on similarity in kernel space. The objective of the scheme is to incorporate multiple features for defining the image indexing space using the concept of multiple kernel learning. However, the indexing problems are defined with unique learning objective; therefore, a novel application of genetic algorithm is presented for the optimization task. The extensive evaluation of the proposed concept is performed for developing word based document indexing application of Devanagari, Bengali, and English scripts. In addition, the efficacy of the proposed concept is shown by experimental evaluations on handwritten digits and natural image collection.