Abstract
The large-scale video data on the web contain a lot of semantics, which are an important part of semantic web. Video descriptors can usually represent somewhat the semantics. Thus, they play a very important role in web multimedia content analysis, such as Scale-invariant feature transform (SIFT) feature. In this paper, we proposed a new video descriptor, called a temporal-compress and shorter SIFT(TC-S-SIFT) which can efficiently and effectively represent the semantics of web videos. By omitting the least discriminability orientation in three stages of standard SIFT on every representative frame, the dimensions of the shorter SIFT are reduced from 128-dimension to 96-dimension to save space storage. Then, the SIFT can be compressed by tracing SIFT features on video temporal domain, which highly compress the quantity of local features to reduce visual redundancy, and keep basically the robustness and discrimination. Experimental results show our method can yield comparable accuracy and compact storage size.
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Zhu, Y., Jiang, C., Huang, X., Xiao, Z., Zhong, S. (2015). A Temporal-Compress and Shorter SIFT Research on Web Videos. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_78
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DOI: https://doi.org/10.1007/978-3-319-25159-2_78
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