A GENERAL FRAMEWORK FOR ROBUST HOSVD-BASED INDEXING AND RETRIEVAL WITH HIGH-ORDER TENSOR DATA

Image and Video Indexing and Retrieval

Přednášející: Dan Schonfeld, Autoři: Qun Li, Xiangqiong Shi, Dan Schonfeld, University of Illinois Chicago, United States

In this paper, we first present a theorem that HOSVD-based representation of high-order tensor data provides a robust framework that can be used for a unified representation of the HOSVD of all subtensors. We then propose a general algorithm for robust indexing and retrieval of multiple motion trajectories obtained from a multi-camera system. Guided by our theorem, the unitary transformation matrices of a subtensor can be very well approximated by a subset of unitary matrices corresponding to the same dimensions of the original tensor. As a result, when dealing with flexible query structure consisting of an arbitrary number of objects and cameras, instead of recalculating unitary transformation matrices of the corresponding subtensor, we can just employ a subset of the original unitary matrices. Simulation results are finally used to illustrate the robustness and efficiency of the proposed approach to multiple trajectory indexing and retrieval from multi-camera systems.

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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5946543

Informace o přednášce

Nahráno:	2011-05-25 17:15 - 17:35, Club A
Přidáno:	14. 6. 2011 22:24
Počet zhlédnutí:	28
Rozlišení videa:	1024x576 px, 512x288 px
Délka videa:	0:10:49
Audio stopa:	MP3 [3.61 MB], 0:10:49