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- W2288533450 abstract "The most addressed issue in person reidentification systems is to manage the performance under large gallery set and invariant appearance. In this study, considering these issues we proposed a color based pre-rank categorization (color basket) for person re-identification. In addition, coarse saliency dense color features were extracted to generate signature. For this purpose, we remove background and apply scaleinvariant feature transform (SIFT) to get convex hull boundary to find the coarse saliency region. Furthermore, an image is divided into three horizontal strips and the dense color features are obtained. For training, incremental LDA (linear discriminant analysis) is utilized and extended with mRMR (minimum Redundancy Maximum Relevance) to handle the high computational cost. The proposed method is evaluated on three publically available datasets, namely i-LIDS, VIPeR and GRID. Moreover, a cumulative matching characteristic (CMC) curve is generated. The curve shows the propose strategy is good in dealing with the above-mentioned problems. Keywords— pre-rank categorizatio; dense color features; LDA; mRMR" @default.
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- W2288533450 date "2016-01-01" @default.
- W2288533450 modified "2023-09-24" @default.
- W2288533450 title "Color Based Pre-rank Categorization for Person Re-identification" @default.
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- W2288533450 doi "https://doi.org/10.2991/icca-16.2016.69" @default.
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