Title :
TV Commercial Categorization Based on Sparse Visual Bag of Words
Author :
Zhenfeng Zhu ; Nan Liu ; Yao Zhao
Author_Institution :
Inst. of Inf. Sci., Beijing Jiaotong Univ., Beijing, China
Abstract :
Automatic TV commercial categorization is the key component of a smart commercial content analysis and understanding system. In this paper, we focus our research on commercial categorization by mining the temporal-spatial context of TV commercial. To boost the discrimination ability of the traditional VBoW in commercial categorization, a more flexible representation, i.e. sparse coding based VBoW (sVBoW), is presented to describe the co-occurrence of semantic units in different kinds of commercials. Meanwhile, both the temporal and spatial cues are fully exploited to reflect the context characteristics of TV commercial. The promising experimental results show the effectiveness of the proposed scheme on commercial categorization.
Keywords :
data mining; feature extraction; image coding; image representation; automatic TV commercial categorization; flexible representation; sVBoW; smart commercial content analysis; sparse coding based VBoW; sparse visual bag of words; temporal-spatial context mining; traditional VBoW; understanding system; Accuracy; Dictionaries; Encoding; Feature extraction; Semantics; TV; Visualization; Bag of Words; TV Commercial Categorization; sparse representation;
Conference_Titel :
Image and Graphics (ICIG), 2013 Seventh International Conference on
Conference_Location :
Qingdao
DOI :
10.1109/ICIG.2013.179
