Saliency Computational Model Research Articles

Saliency computation via whitened frequency band selection.

Many saliency computational models have been proposed to simulate bottom-up visual attention mechanism of human visual system. However, most of them only deal with certain kinds of images or aim at specific applications. In fact, human beings have the ability to correctly select attentive focuses of objects with arbitrary sizes within any scenes. This paper proposes a new bottom-up computational model from the perspective of frequency domain based on the biological discovery of non-Classical Receptive Field (nCRF) in the retina. A saliency map can be obtained according to the idea of Extended Classical Receptive Field. The model is composed of three major steps: firstly decompose the input image into several feature maps representing different frequency bands that cover the whole frequency domain by utilizing Gabor wavelet. Secondly, whiten the feature maps to highlight the embedded saliency information. Thirdly, select some optimal maps, simulating the response of receptive field especially nCRF, to generate the saliency map. Experimental results show that the proposed algorithm is able to work with stable effect and outstanding performance in a variety of situations as human beings do and is adaptive to both psychological patterns and natural images. Beyond that, biological plausibility of nCRF and Gabor wavelet transform make this approach reliable.

A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

With the emerging development of three-dimensional (3D) related technologies, 3D visual saliency modeling is becoming particularly important and challenging. This paper presents a new depth perception and visual comfort guided saliency computational model for stereoscopic 3D images. The prominent advantage of the proposed model is that we incorporate the influence of depth perception and visual comfort on 3D visual saliency computation. The proposed saliency model is composed of three components: 2D image saliency, depth saliency and visual comfort based saliency. In the model, color saliency, texture saliency and spatial compactness are computed respectively and fused to derive 2D image saliency. Global disparity contrast is considered to compute depth saliency. Particularly, we train a visual comfort prediction function to distinguish stereoscopic image pair as high comfortable stereo viewing (HCSV) or low comfortable stereo viewing (LCSV), and devise different computational rules to generate a visual comfort based saliency map. The final 3D saliency map is obtained by using a linear combination and enhanced by a “saliency-center bias” model. Experimental results show that the proposed 3D saliency model outperforms the state-of-the-art models on predicting human eye fixations and visual comfort assessment.

Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features

Saliency computational model with active environment perception can be useful for many applications including image retrieval, object recognition, and image segmentation. Previous work on bottom-up saliency computation typically relies on hand-crafted low-level image features. However, the adaptation of saliency computational model towards different kinds of scenes remains a challenge. For a low-level image feature, it can contribute greatly on some images but may be detrimental for saliency computation on other images. In this work, a novel data driven approach is proposed to adaptively select proper features for different kinds of images. This method exploits low-level features containing the most distinguishable salient information per image. Then the image saliency can be calculated based on the adaptive weight selection scheme. A large number of experiments are conducted on the MSRA database to compare the performance of the proposed method with the state-of-the-art saliency computational models.

Saliency-guided neural prosthesis for visual attention: Design and simulation

Recently the authors showed that a computational model of visual saliency could account for changes in gaze behavior of monkeys with damage in the primary visual cortex. Here we propose a neural prosthesis to restore eye gaze behavior by electrically stimulating the superior colliculus to drive visual attention. The saliency computational model is used to calculate the stimulation parameters from a real-time camera stream. Our simulations demonstrate that electrodes implanted in the superior colliculus at 1.0mm spacing are, in principle, able to recover specifically those visual attention behaviors which are lost when the primary visual cortex is damaged.