Generalization of Determinant Kernels for Non-Square Matrix and its Application in Video Retrieval

Mohammad Jafari¹ , Neda Abdollahi² , Ali Amiri³ , Mahmood Fathy⁴

Section:Research Paper, Product Type: Isroset-Journal
Vol.3 , Issue.4 , pp.1-6, Jul-2015

Online published on Sep 08, 2015

Copyright © Mohammad Jafari, Neda Abdollahi, Ali Amiri, Mahmood Fathy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
For a specific set of features selected for representing videos, the performance of a content-based video retrieval system depends critically on the similarity or dissimilarity measures used. In this paper, we propose a kernel approach to improve the retrieval performance of content-based video retrieval systems namely determinant kernel. The input of this kernel is the dot product of feature matrices that extracted from shot visual information. Due to the variation in the number of each shot frames, the size of feature matrices are different and so the result of dot product become a non-square matrices. Almost all available techniques use summarizing methods to equalize the size of the matrices which lead to loss some parts of information. To solve this problem, we present a non-square determinant kernel based on Radic’s definition. We evaluate the performance of the derived Kernels by retrieving video shots of news and speaking videos. Experimental results confirm the effectiveness of our proposed algorithm.

Key-Words / Index Term :
Content-based video retrieval; determinant kernel; non-square matrices; similarity measurement; feature matrix

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