Enhancing Saliency of an Object Using Genetic Algorithm

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.

[1]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Rajarshi Pal,et al.  Constrained maximization of saliency of intended object for guiding attention , 2015, 2015 Annual IEEE India Conference (INDICON).

[3]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Yasue Mitsukura,et al.  Color image modification based on visual saliency for guiding visual attention , 2013, 2013 IEEE RO-MAN.

[5]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[6]  Pabitra Mitra,et al.  ICam: Maximizes Viewers' Attention on Intended Objects , 2008, PCM.

[7]  Amitabh Varshney,et al.  Saliency-guided Enhancement for Volume Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

[8]  Ivan V. Bajic,et al.  Attention Retargeting by Color Manipulation in Images , 2014, PIVP '14.

[9]  A. Sugimoto,et al.  Saliency-based image editing for guiding visual attention , 2011, PETMEI '11.

[10]  Steven K. Feiner,et al.  Directing attention and influencing memory with visual saliency modulation , 2011, CHI.

[11]  Jin-Jang Leou,et al.  Visual attention region determination using low-level features , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[12]  Derrick J. Parkhurst,et al.  Scene content selected by active vision. , 2003, Spatial vision.

[13]  Takuji Narumi,et al.  Attracting User's Attention in Spherical Image by Angular Shift of Virtual Camera Direction , 2015, SUI.

[14]  Shigeo Takahashi,et al.  Enhancing Infographics Based on Symmetry Saliency , 2016, VINCI.

[15]  Ann McNamara,et al.  Guiding attention in controlled real-world environments , 2013, SAP.

[16]  Ann McNamara,et al.  Subtle gaze direction , 2007, SIGGRAPH '07.

[17]  Yoichi Sato,et al.  Visual Guidance with Unnoticed Blur Effect , 2016, AVI.

[18]  Pabitra Mitra,et al.  Modelling visual saliency using degree centrality , 2010 .

[19]  P Reinagel,et al.  Natural scene statistics at the centre of gaze. , 1999, Network.

[20]  Brendan John,et al.  Gaze guidance for improved password recollection , 2016, ETRA.

[21]  Frédo Durand,et al.  De-emphasis of distracting image regions using texture power maps , 2005, APGV '05.