Abstract
A content-based video retrieval via visual feature pooling is proposed in this paper. Since these visual words represent local features extracted from frame images, spatio-temporal constrains are applied to solve the ambiguity of the model towards effective retrieval of semantic video clips. Both shot level and segment level processing are employed, and the latter is found more robust in dealing with complex scenes where accurate video segmentation may fail. Our experimental results have shown that the constrained scheme help to improve 5 % average matching accuracy. In addition, it suggests that summarized videos at 25–30 % of original size can still maintain a viewing quality of 70–80 % towards fast content delivery.
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Ren, J., Ren, J. (2016). Feature Pooling Using Spatio-Temporal Constrain for Video Summarization and Retrieval. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_50
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DOI: https://doi.org/10.1007/978-981-10-1536-6_50
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