NDLI: Shot classification for action movies based on motion characteristics (2008)

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Shot classification for action movies based on motion characteristics (2008)

Content Provider	CiteSeerX
Author	Wang, Shuhui Jiang, Shuqiang Huang, Qingming Gao, Wen
Description	In International Conference on Image Processing In this paper, we propose a shot classification method for action movies. Considering that motion characteristic is very important for semantic movie analysis, and it contains abundant information in action movies, the structure tensor analysis is used for feature extraction due to its capability of representing both spatial and temporal characteristics of a shot. Firstly, the movie shots with known labels are de-composed into a set of overlapped fixed-length segments and their structure tensor histogram are computed. The labels of segments are identical to the shots they belong to. Then Adaboost is used to train the semantic classifier with these structure tensor histogram sets. In testing procedure, the unknown shot are decomposed in the same way, and feature vector of each segment is extracted and classified by the classifier. Finally, the label of the shot is generalized by the segment label voting scheme. Experimental results show that this scheme could effectively deal with multiple motion patterns within shots and promising results are achieved.1 Index Terms—Action Movies, Shot classification, Structure tensor feature, Adaboost, and Voting Scheme
File Format	PDF
Language	English
Publisher Date	2008-01-01
Access Restriction	Open
Subject Keyword	Feature Vector Structure Tensor Analysis Semantic Classifier Action Movie Feature Extraction Structure Tensor Histogram Multiple Motion Pattern Overlapped Fixed-length Segment Motion Characteristic Movie Shot Promising Result Semantic Movie Analysis Segment Label Voting Scheme Temporal Characteristic Voting Scheme Structure Tensor Histogram Set Shot Classification Method Abundant Information Index Term Action Movie Unknown Shot Shot Classification Experimental Result Structure Tensor Feature
Content Type	Text
Resource Type	Article

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