SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations

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  9. Henry, Christopher J.; Ramanna, Sheela: Signature-based perceptual nearness: application of near sets to image retrieval (2013)
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  15. Yu, Zhiwen; Wong, Hau-San; You, Jane; Han, Guoqiang: Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme (2012) ioport
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  17. Zhang, Dengsheng; Islam, M. Monirul; Lu, Guojun; Sumana, Ishrat Jahan: Rotation invariant curvelet features for region based image retrieval (2012) ioport
  18. Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W. S.: Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction (2012)
  19. Bulò, Samuel Rota; Rabbi, Massimo; Pelillo, Marcello: Content-based image retrieval with relevance feedback using random walks (2011) ioport
  20. Ciocca, Gianluigi; Cusano, Claudio; Santini, Simone; Schettini, Raimondo: Halfway through the semantic gap: prosemantic features for image retrieval (2011) ioport

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