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. Anitha, K.; Dhanalakshmi, R.; Naresh, K.; Rukmani Devi, D.: Hyperbolic Hopfield neural networks for image classification in content-based image retrieval (2021)
  2. Hosny, Khalid M.; Darwish, Mohamed M.; Aboelenen, Tarek: Novel fractional-order polar harmonic transforms for gray-scale and color image analysis (2020)
  3. Singh, Chandan; Singh, Jaspreet: Geometrically invariant color, shape and texture features for object recognition using multiple kernel learning classification approach (2019)
  4. Chen, Jen-Hao; Hung, Wen-Liang: An automatic clustering algorithm for probability density functions (2015)
  5. Jang, Min-Hee; Kim, Sang-Wook; Faloutsos, Christos; Park, Sunju: Accurate approximation of the earth mover’s distance in linear time (2014) ioport
  6. Krishnamoorthy, R.; Kalpana, J.: Fast retrieval of color objects with multidimensional orthogonal polynomials (2014) ioport
  7. Li, Longjie; Ma, Min; Lei, Peng; Wang, Xiaoping; Chen, Xiaoyun: A linear approximate algorithm for Earth Mover’s distance with thresholded ground distance (2014)
  8. Manipoonchelvi, P.; Muneeswaran, K.: Multi region based image retrieval system (2014)
  9. Shrivastava, Nishant; Tyagi, Vipin: Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching (2014) ioport
  10. Fashandi, Homa: Nearness of covering uniformities: theory and application in image analysis (2013)
  11. Henry, Christopher J.; Ramanna, Sheela: Signature-based perceptual nearness: application of near sets to image retrieval (2013)
  12. Zhu, Chao; Bichot, Charles-Edmond; Chen, Liming: Image region description using orthogonal combination of local binary patterns enhanced with color information (2013) ioport
  13. 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) ioport
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  15. Qin, Jianzhao; Yung, Nelson H. C.: Feature fusion within local region using localized maximum-margin learning for scene categorization (2012) ioport
  16. Yao, Lei; Suryanarayan, Poonam; Qiao, Mu; Wang, James Z.; Li, Jia: OSCAR: on-site composition and aesthetics feedback through exemplars for photographers (2012) ioport
  17. 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
  18. Zhang, Dengsheng; Islam, Md. Monirul; Lu, Guojun: A review on automatic image annotation techniques (2012) ioport
  19. Zhang, Dengsheng; Islam, M. Monirul; Lu, Guojun; Sumana, Ishrat Jahan: Rotation invariant curvelet features for region based image retrieval (2012) ioport
  20. Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W. S.: Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction (2012)

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