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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  1. Jang, Min-Hee; Kim, Sang-Wook; Faloutsos, Christos; Park, Sunju: Accurate approximation of the earth mover’s distance in linear time (2014)
  2. Krishnamoorthy, R.; Kalpana, J.: Fast retrieval of color objects with multidimensional orthogonal polynomials (2014)
  3. Manipoonchelvi, P.; Muneeswaran, K.: Multi region based image retrieval system (2014)
  4. Shrivastava, Nishant; Tyagi, Vipin: Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching (2014)
  5. Fashandi, Homa: Nearness of covering uniformities: theory and application in image analysis (2013)
  6. Henry, Christopher J.; Ramanna, Sheela: Signature-based perceptual nearness: application of near sets to image retrieval (2013)
  7. Iqbal, Kashif; Odetayo, Michael O.; James, Anne: Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics (2012)
  8. Ivanescu, Anca Maria; Wichterich, Marc; Beecks, Christian: The ClasSi coefficient for the evaluation of ranking quality in the presence of class similarities (2012)
  9. Qin, Jianzhao; Yung, Nelson H.C.: Feature fusion within local region using localized maximum-margin learning for scene categorization (2012)
  10. Yao, Lei; Suryanarayan, Poonam; Qiao, Mu; Wang, James Z.; Li, Jia: OSCAR: on-site composition and aesthetics feedback through exemplars for photographers (2012)
  11. Yu, Zhiwen; Wong, Hau-San; You, Jane; Han, Guoqiang: Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme (2012)
  12. Zhang, Dengsheng; Islam, Md.Monirul; Lu, Guojun: A review on automatic image annotation techniques (2012)
  13. Zhang, Dengsheng; Islam, M.Monirul; Lu, Guojun; Sumana, Ishrat Jahan: Rotation invariant curvelet features for region based image retrieval (2012)
  14. Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W.S.: Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction (2012)
  15. Bulò, Samuel Rota; Rabbi, Massimo; Pelillo, Marcello: Content-based image retrieval with relevance feedback using random walks (2011)
  16. Ciocca, Gianluigi; Cusano, Claudio; Santini, Simone; Schettini, Raimondo: Halfway through the semantic gap: prosemantic features for image retrieval (2011)
  17. da Silva, André Tavares; Falcão, Alexandre Xavier; Magalhães, Léo Pini: Active learning paradigms for CBIR systems based on optimum-path forest classification (2011)
  18. Ilea, Dana E.; Whelan, Paul F.: Image segmentation based on the integration of colour-texture descriptors -- a review (2011)
  19. Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri; Alimi, Mohamed Adel: Pyramidal hybrid approach: wavelet network with OLS algorithm-based image classification (2011)
  20. Ramanna, Sheela; Meghdadi, Amir H.; Peters, James F.: Nature-inspired framework for measuring visual image resemblance: a near rough set approach (2011)

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