A Survey of an Adaptive Weighted Spatio-Temporal Pyramid Matching For Video Retrieval
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2013, Vol 6, Issue 3
Abstract
Recently, in the field of video analysis and retrieval Human action recognition in video is an important research and challenging topic. An efficient video retrieval is needed to search most similar and relevant video contents of a large set of video clips. Many methods have been used for the efficient retrieval of videos. In this Adaptive weighted pyramid matching kernel (AWPM) has been used for efficiently retrieving videos by recognizing human actions in realistic videos. This can be done based on a multi channel bag of words which is constructed from local spatial-temporal features of video clips. AWPM is the extension of spatial-temporal pyramid matching (STPM) kernel leverages in spatio-temporal granularity level and in multiple feature descriptor types to build a suitable similarity metric between two video clips. STPM uses predefined and fixed weights and hence the proposed matching algorithm estimates adopts channel of weights based on the Kernel target alignment of training data. The following work is analysis over the content based video retrieval in large database through various mechanisms available in the literature.
Authors and Affiliations
Priya Iype
Keywords
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