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|Title:||Video-based pattern recognition by spatio-temporal modeling via multi-modality co-learning|
Image processing -- Digital techniques.
Pattern recognition systems.
Hong Kong Polytechnic University -- Dissertations
|Publisher:||The Hong Kong Polytechnic University|
|Abstract:||Secondly we extend our work by utilizing local spatio-temporal features via indexing. Local features generally contain more statistical information for discrimination. We deal with the spatio-temporal modeling by partitioning appearance space. The proposed approach can capture the discriminative information among different action classes. For trajectory matching solution, we develop a query-driven dynamic appearance modeling method and use localized subspaces to obtain more reliable distance for discrimination. Flexibility is also guaranteed by introducing a warping scheme. The processing is implemented based on an indexing scheme, which is very fast in computation. Simulation results demonstratethe effectiveness of the solution. Thirdly we focus on improving the pattern recognition performance by proposing novel learning methods. Consider the various features used for video representation, we target on utilizing multiple set of features to jointly solve the recognition problem. We propose a multi-modality distance metric co-learning method. Two set of different features are jointly utilized to generate a better description the video clips. In this way the similarity between video clips is better evaluated and the recognition accuracy is improved. The effectiveness of proposed method is proved by audio-visual speaker identification. Furthermore, to demonstrate the robustness, the proposed method is also applied on digit recognition and text classification. Experiment results show the proposed multi-modality result is better than single modality, together with other previous method in recognition accuracy.|
|Description:||xiv, 105 p. : ill. ; 30 cm.|
PolyU Library Call No.: [THS] LG51 .H577P COMP 2012 ZhengH
|Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Checked on Apr 16, 2017
Checked on Apr 16, 2017
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