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- W2024371346 abstract "Nowadays, a strong need exists for the efficient organization of an increasing amount of home video content. To createan efficient system for the management of home video content, it is required to categorize home video content in asemantic way. So far, a significant amount of research has already been dedicated to semantic video categorization.However, conventional categorization approaches often rely on unnecessary concepts and complicated algorithms thatare not suited in the context of home video categorization. To overcome the aforementioned problem, this paperproposes a novel home video categorization method that adopts semantic home photo categorization. To use home photocategorization in the context of home video, we segment video content into shots and extract key frames that representeach shot. To extract the semantics from key frames, we divide each key frame into ten local regions and extract lowlevelfeatures. Based on the low level features extracted for each local region, we can predict the semantics of aparticular key frame. To verify the usefulness of the proposed home video categorization method, experiments wereperformed with home video sequences, labeled by concepts part of the MPEG-7 VCE2 dataset. To verify the usefulnessof the proposed home video categorization method, experiments were performed with 70 home video sequences. For thehome video sequences used, the proposed system produced a recall of 77% and an accuracy of 78%." @default.
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- W2024371346 date "2009-02-05" @default.
- W2024371346 modified "2023-09-23" @default.
- W2024371346 title "Semantic home video categorization" @default.
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- W2024371346 doi "https://doi.org/10.1117/12.806104" @default.
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