Generalization of Determinant Kernels for Non-Square Matrix and its Application in Video Retrieval
Mohammad Hossein Jafari, Neda Abdollahi, Ali Amiri, Mahmood Fathy · International journal of scientific research · 2015
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.