I.Jeena Jacob, SCAD College of Engg. and Tech; V.Elavarasi ,

Image retrieval, Visual attention, Saliency Structure Model

A novel mechanism to simulate visual attention mechanisms for content-based image retrieval, based on saliency structure histogram method was proposed in this paper. In CBIR, images are indexed by their visual content, such as color, texture, shapes. A color volume with edge information together is used to detect saliency regions. The texture image features, such as energy, inverse difference moment, contrast are extracted. To simulate orientation-selective mechanism for image representation within CBIR framework, saliency structure histogram is used. The performance of the proposed algorithm was evaluated based on two datasets. The proposed algorithm outperforms the standard BOW baseline and micro-structure descriptor.
    [1] Hubel, T.N. Wiesel, Receptive fields. Binocular interaction and functional architecture in the cat's visual cortex, J. Physiol. 160 (1962) 106–154. [2] E.H. Adelson, J.R. Bergen., The plenoptic function and the elements of early vision, in: M. Landy, J. Movshon (Eds.), Computational Models of Visual Processing, MIT Press, Cambridge, 1991, pp. 3–20. [3] G.-H. liu, Z.-Y. Li, L. zhang, Y. Xu, Image retrieval based on micro-structure descriptor, Pattern Recognit. 44 (9) (2011) 2123–2133. [4] Treisman., A feature in integration theory of attention, Cogn. Psychol. 12 (1) (1980) 97–136. [5] J..Sivic, A. Zisserman, Video Google: A text retrieval approach to object matching in videos, in: Proceedings of IEEE International Conference on In Computer Vision, 2003 [6] L. Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell. 20 (11) (1998) 1254–1259. [7] Borji, L. Itti. Exploiting local and global patch rarities for saliency detection, in: 2012 IEEE conference on computer vision and pattern recognition, 2012, pp. 478–485 [8] Toet, Computational versus psychophysical bottom-up image saliency: a comparative evaluation study, IEEE Trans. Pattern Anal. Mach. Intell. 33 (11) (2011) 2131–2146. [9] R.M. Haralick, Dinstein Shangmugam, Textural feature for image classification, IEEE Trans. Syst. Man Cybern. SMC-3 (6) (1973) 610–621. [10] C.j.van Rijsbergen, Informaton Retrieval, Butterworths, London,1979. [11] G. Hripcsak, A.S. Rothschild, Agreement: the f-measure, and reliability in information retrieval, j.Am.Med.Inf.Assoc,12(3) (2005) 296-298.
Paper ID: GRDCF002024
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 99 - 102