Image saliency detection is a method used to eliminate the redundant image information. Moreover

this method is used in many computer vision applications

such as adaptive compression of images

content-aware image editing

and image retrieval. In this study

a new image saliency detection method is proposed to compute for image saliency from different perspectives. In fact

many methods are used to compute for saliency

and most of these approaches use different types of features to detect saliency in single regional representation. However

only a few methods consider the adaptability between the feature and image representation. According to the different characters of different types of regional representations

we compute image saliency from different angles by using a wide variety of information

including color. The method consists of three basic steps. First

the image is mapped from the pixel space to a two-layer regional representation space on the basis of connectivity and edge information. The first layer is related to the spatial structure of the image

whereas the second one is superior in describing color information. Then

on the basis of the diverse properties of the constructed two-layer representations

we adopt a number of features to abstract image saliency. In the first layer

we use the spatial distribution of region in the image and the structure feature to obtain the spatial structure saliency. In the second layer

we use the color feature to compute for color saliency. Given the complementarity between the two kinds of saliency

the last step is to integrate the two kinds to obtain the final saliency map. In practice

color saliency has higher significance and discriminative power than spatial structure saliency. Thus

we use an exponential function to combine the two kinds of saliency while highlighting color saliency. In addition

the boundary prior is also a reasonable and popular method for enhancing saliency detection and has thus been widely used for image saliency detection. In contrast to existing methods that set a region containing boundary pixels directly to the background

we employ the percentage of boundary pixels in each region to adjust saliency values. Given that the extracted saliency clues correspond to the attributes of the local image regions quite well

our method has several advantages over existing methods. To verify the efficiency of the proposed method

experiments are performed using the MSRA-1000 dataset

which is one of the largest publicly available datasets. Results show that our method outperforms state-of-the-art methods in terms of precision

recall

F-measure

and mean absolute error. Image saliency detection is a promising approach in the field of image processing and analysis. This study presents a new saliency detection method based on two-layer regional representation through both color saliency and spatial structure saliency. Experimental evaluation results suggest that our method outperforms other methods in image saliency detection.