Usage of GBVS in Image Processing to Retrieve the Images
DOI:
https://doi.org/10.26438/ijcse/v6i3.138142Keywords:
Image Processing, Image Retrieval, Shape, Color, Graph based visual saliency, Content Based Image retrievalAbstract
In the Modern world, the propensity of the detecting most Salient objects are trending on a large scale. In the era of Computer and science and specially in the Image Processing, we tend to find out most feasible techniques which can extract the most relevant and salient features of the selected image database. We are provided with a few most usable image retrieval methods however still we are not satisfied with the output extracted from the used method. With the advancement of the technology and the Image processing techniques, we have new methods to highlight the Salient features. One of them is Graph based Visual Saliency. GBVS is the technique which produces the salient features in a very accurate and faster way and in an elaborated way. It produces the data in the activation map and then extracts the features from the original image. We have here used a few different images and using our proposed method tried to depict the results in a graphical and pictorial way. Our effort main motive is to highlight the features of an image in a wider manner. In this paper, we would learn how to show Salient part of an image but in a large scale. In this paper, it shows 80% Salient part of an image of GBVS.
References
L. Itti, C. Koch, E. Niebur, et al.“A saliency-based search mechanism for overt and covert shifts of visual attention”1999.
Hans-Christoph Nothdurft* “Salience from feature contrast: temporal properties of saliency mechanisms” May 1999.
RitendraDattaJia Li James Z. Wang “Content-Based Image Retrieval Approaches and Trends of the New Age”2005.
D.Walther and C. Koch. “Modeling attention to salient proto-objects” Published by Elsevier Ltd 2006.
Kanakam Siva Ram Prasad, "New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.16-21, 2017.
Tamura,Hideyuki,Mori,Ymawaki,Shunji,Takashi,“Textual Features corresponding to visual perception”,Vol 8,Isssue 6, Systems Man And Cybernetics,in IEEE transactions,2007.
Mustafa Ozden, EdizPolat, “A colour image segmentation approach for content-based image retrieval, Pattern Recognition”2007.
WeilongHoua, XinboGaoa, Dacheng Taob1*, XuelongLic “Visual Saliency Detection Using Information Divergence”2007.
A. Agarwal, S.S. Bhadouria, "An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.56-62, 2017.
Lin Zhang, ZhongyiGu, and Hongyu Li*1“A novel saliency detection method by combining simple priors” 978-1-4799-2341-0/13/$31.00 IEEE ©2013.
Qiong Yan Li Xu Jianping Shi JiayaJia “Hierarchical Saliency Detection”2013.
Anita N. Ligade, Manisha R. Patil “Content Based Image Retrieval Using Interactive Genetic Algorithm with Relevance Feedback” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) ,2014.
ChestiAltaff Hussain, S.AranaMasthani “Robust pre-processing technique based on saliency detection for content based image retrieval systems” Volume 85 published by Elsevier,2016.
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