Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4639
Title: Enhancing Saliency of an Object Using Genetic Algorithm
Authors: Roy, Dipanjan
Keywords: Behavioral research;Computer vision;Eye tracking;Genetic algorithms;Image segmentation;Petroleum reservoir evaluation;Visualization;attention;Experimental validations;Feature values;Maximization problem;Objective evaluation;Target object;Visual Attention;Visual saliency;Image enhancement
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Pal, R., & Roy, D. (2018). Enhancing saliency of an object using genetic algorithm. Paper presented at the Proceedings - 2017 14th Conference on Computer and Robot Vision, CRV 2017, , 2018-January 337-344. doi:10.1109/CRV.2017.33
Abstract: It is often required to emphasize an object in an image. Artists, illustrators, cinematographers and photographers have long used the principles of contrast and composition to guide visual attention. In order to achieve this, a novel perceptually-driven approach is put forth which leads to the enhancement of visual saliency of target object without destroying the naturalness of the contents of the image. The proposed approach computes new feature values for the intended object by maximizing the feature dissimilarity (which is weighted by positional proximity) with other objects. Too much change in feature values in the target segment may destroy naturality of the image. This poses as the constraint in the proposed maximization problem. Genetic algorithm has been used, in this context, to find the feature values which maximize the saliency of the target object. Experimental validation through objective evaluation metrics using saliency maps, as well as analysis of eye-tracking data, establish the success of the proposed method. © 2017 IEEE.
URI: https://doi.org/10.1109/CRV.2017.33
https://dspace.iiti.ac.in/handle/123456789/4639
ISBN: 9781538628188
Type of Material: Conference Paper
Appears in Collections:Department of Computer Science and Engineering

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