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- W2462477420 abstract "The feature descriptors such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) are known as the most commonly used solutions for the content-based image retrieval problems. In this paper, a novel approach called ”Weighted Feature Fusion” is proposed as a generic solution instead of applying problem-specific descriptors alone. Experiments were performed on two basic data sets of the Inria in order to improve the precision of retrieval results. It was found that in cases where the descriptors were used alone the proposed approach yielded 10-30% more accurate results than the ORB alone. Besides, it yielded 9-22% and 12-29% less False Positives compared to the SIFT alone and SURF alone, respectively." @default.
- W2462477420 created "2016-07-22" @default.
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- W2462477420 date "2016-07-11" @default.
- W2462477420 modified "2023-09-23" @default.
- W2462477420 title "Weighted feature fusion for content-based image retrieval" @default.
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- W2462477420 doi "https://doi.org/10.1117/12.2242956" @default.
- W2462477420 hasPublicationYear "2016" @default.
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