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- W3136102207 abstract "New visual and static features, namely, right singular feature vector, left singular feature vector and singular valuefeature vector are proposed for the semantic concept detection in images. These features are derived by applying singularvalue decomposition (SVD) directly to the raw images. In SVD features edge, color and texture information isintegrated simultaneously and is sorted based on their importance for the concept detection. Feature extraction isperformed in a multi-granularity partitioning manner. In contrast to the existing systems, classification is carried out foreach grid partition of each granularity separately. This separates the effect of classifications on partitions with and withoutthe target concept on each other. Since SVD features have high dimensionality, classification is carried out with K-nearestneighbor (K-NN) algorithm that utilizes a new and stable distance function, namely, multiplicative distance.Experimental results on PASCAL VOC and TRECVID datasets show the effectiveness of the proposed SVD features andmulti-granularity partitioning and classification method" @default.
- W3136102207 created "2021-03-29" @default.
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- W3136102207 date "2017-09-01" @default.
- W3136102207 modified "2023-09-23" @default.
- W3136102207 title "Concept Detection in Images Using SVD Features and Multi-Granularity Partitioning and Classification" @default.
- W3136102207 doi "https://doi.org/10.7508/jist.2017.19.004" @default.
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