Abstract
Researches on visual attention mechanism have revealed that the human visual system (HVS) is sensitive to the higher frequency components where they are distinctive from their surroundings by popping out. These attentive components of the scene can be in any form such as edge to texture differences based on the focus of attention of HVS. There are several visual attention computational models that can yield saliency values of attentive regions on the image. Some of these models take advantage of band-pass filter regions on spatial domain by computing center-surround differences with difference of low pass filters. They use either down-sampling that may cause loss of information or constant scale of the filters that may not contain all the necessary saliency information from the image. Therefore, we proposed an efficient and simple saliency detection model with full resolution and high perceptual quality, which outputs several band-pass regions by utilizing Fourier transform to obtain attentive regions edges to textures from the color image. All these detected important information with different bandwidth, then, were fused in a weighted manner by giving more priority to the texture compared to edge based salient regions. Experimental analysis was made for different color spaces and the model was compared with some relevant state of the art algorithms. As a result, the proposed saliency detection model has promising results based on the area under curve (AUC) performance evaluation metric.
Highlights
The human visual system (HVS) tends to focus its attention on the regions that pop-out significantly compared to their surroundings on the scene [1,2]
Researches on visual attention mechanism have revealed that the human visual system (HVS) is sensitive to the higher frequency components where they are distinctive from their surroundings by popping out
Evaluation process was done by using a dataset which consists of 1000 images and their ground-truths of segmented object regions [4]
Summary
The human visual system (HVS) tends to focus its attention on the regions that pop-out significantly compared to their surroundings on the scene [1,2]. Top-down approach is relatively slower and task-dependent mechanism with prior knowledge that may require both low-level and high-level features [2]. These attentive regions can benefit to fast scene analysis, such as detection of proto-objects [3] or segmentation [4], so several computational models have been developed [3,4,5,6,7,8] since the first proposed model of Itti, Koch, and Niebur [5]. This biologically plausible model has become inspiration for several studies of the saliency computational models in spatial or transform domains [1,2,3,4,6,7,8], where spatial domain models take advantage of center-surround differences or contrast for the salient region detection
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