Guiding attention of faces through graph based visual saliency (GBVS)

Abstract

In a general scenario, while attending a scene containing multiple faces or looking towards a group photograph, our attention does not go equal towards all the faces. It means, we are naturally biased towards some faces. This biasness happens due to availability of dominant perceptual features in those faces. In visual saliency terminology it can be called as 'salient face'. Human's focus their gaze towards a face which carries the 'dominating look' in the crowd. This happens due to comparative saliency of the faces. Saliency of a face is determined by its feature dissimilarity with the surrounding faces. In this context there is a big role of human psychology and its cognitive science too. Therefore, enormous researches have been carried out towards modeling the computer vision system like human's vision. This paper proposed a graphical based bottom up approach to point up the salient face in the crowd or in an image having multiple faces. In this novel method, visual saliencies of faces have been calculated based on the intensity values, facial areas and their relative spatial distances. Experiment has been conducted on gray scale images. In order to verify this experiment, three level of validation has been done. In the first level, our results have been verified with the prepared ground truth. In the second level, intensity scores of proposed saliency maps have been cross verified with the saliency score. In the third level, saliency map is validated with some standard parameters. The results are found to be interesting and in some aspects saliency predictions are like human vision system. The evaluation made with the proposed approach shows moderately boost up results and hence, this idea can be useful in the future modeling of intelligent vision (robot vision) system.

Motivational salience drives habitual gazes during value memory retention and facilitates relearning of forgotten value

“…Next, we investigated what components might guide the change of habitual gaze over time. The graph-based visual saliency method (GBVS) with perceptual salience (PS) can predict the innate gaze bias of human participants from low-level visual features ( Harel et al., 2007 ; Kumar et al., 2019 ). During free-viewing conditions, the innate gaze bias to presented objects before learning was successfully predicted using the GBVS method ( Figure S3 A).…”

Bio-Inspired Representation Learning for Visual Attention Prediction

“…Earlier works devote to investigate the VAP task based on static images [25]- [29]. These works are mostly on the basis of bottom-up visual attention mechanism [25], [26], [30], [31].…”

“…Earlier works devote to investigate the VAP task based on static images [25]- [29]. These works are mostly on the basis of bottom-up visual attention mechanism [25], [26], [30], [31]. Itti et al [9] firstly conduct the VAP task by imitating the human bottom-up visual selective attention process to extract the low-level visual features of images.…”

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

“…This method retained the visual feature extraction method of Itti model but used the Markov transfer matrix to measure the saliency map. Kumar et al [23] used the manifold sorting of graphs to further improve the accuracy of saliency maps. Since the distribution of local features and global features of images is usually inversely proportional, Liu et al [24] proposed MRDCR (Multi-Resolution Dictionary Collaborative Representation) to represent the test image at different resolutions.…”