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- W2809866997 abstract "Techniques of bags-of-visual-words based on signature have been employed in image retrieval and analysis, with the benefit of dismissing expensive clustering processes. However, the limitations of such techniques are the requirement of multiple parameters, which may be unintuitive and in most cases depends on the application domain. In this paper, we overcome these limitations by proposing Bag-of-Superpixel Signatures (BoSS), which extracts visual signatures using local features from superpixels. Moreover, our proposal also employs a fractal analysis to extract intrinsic information about the domain application and also to diminish the amount of parameters needed. The results demonstrated that BoSS achieved an improvement up to 31.2% in image retrieval precision during experimental evaluations over five distinct datasets. We conclude that BoSS introduces an intuitive, self-contained, scalable and effective approach for image retrieval using bags-of-visual words." @default.
- W2809866997 created "2018-07-10" @default.
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- W2809866997 date "2018-04-09" @default.
- W2809866997 modified "2023-09-25" @default.
- W2809866997 title "BoSS" @default.
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- W2809866997 doi "https://doi.org/10.1145/3167132.3167374" @default.
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