## 1 Introduction
Among them, CS-LBP (Center Symmetric Local Binary Pattern) [11] and CS-LTP (Center Symmetric Local Trainry Pattern) [12] are popular representatives. In these two descriptors, the histogram that encodes the local texture feature of the region surrounding each pixel is constructed with the same number of bins, spreading over the full texton spectrum domain. It is not difficult to understand that, as the number of pixels involved increases, the computational load to compute the corresponding histograms also increases, which makes descriptors constructed in a similar way unsuitable for describing the region feature. Another problem with this histogram encoding is that it is not efficient. Because practically every local region contains a few types of textures, it is not necessary to generate the histogram over the whole range of the texton  
Figure 2: Examples of saliency maps generated by FT, HC, RC, SF and FASA methods. spectrum to avoid bringing in additional calculations. In addition, the histogram as constructed is a measure of the degree of similarity, not an indication of the differences of contrast among the local regions, and therefore it cannot be used for calculating the region contrast on which the saliency detection we propose is based. Superpixel segmentation is now a commonly used image segmentation method. To some extent, a superpixel can be regarded as a region. Compared to the local region, as discussed above and normally selected as a circle surrounding each pixel with certain radius, a superpixel often has an irregular shape that contains the inherent feature of an object. Meanwhile, regarding a superpixel as a region, decreases the number of regions, implying a certain reduction in calculations compared to the pixel-based reionization methods. Based on the analyses and discussions that have been conducted, a Local Texture-based Region Sparse Histogram model (LTRSH) is proposed in this paper. The model is superpixel based, and a combination of two histograms, one for local texture information and contour information and the other for color information, is used to describe the features of superpixels. The histograms are constructed on the base of sparse representation to enhance efficiency of the processing, and as created, they are also capable of measuring the differences of contrast among the local regions, which facilitates the computation of the region contrast. Extensive experimentation has been carried out to evaluate the method proposed in this paper against five state-of-the-art saliency detection methods [6; 7; 8; 9; 10] on benchmark datasets. The comparative results show that the proposed method is promising, as it improves the detection performance in terms of precision rate, recall rate, and F\({}_{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \texttexttext  
generated average color difference in the multi-scale space from the average of their neighborhoods as the saliency (Average Contrast (AC)). Although a full-resolution salient map is presented, the precision and location are not accurate and appear to be sensitive to noise and complex texture. Liu _et al._[19] obtained the saliency via multi-scale contrast and then linearly combined the contrast in a Gaussian image pyramid. In 2013, Li _et al._[20] detected saliency by finding the vertices and hyperedges in a previously constructed hypergraph. In 2015, Lin _et al._[21] addressed the problem of saliency detection by learning adaptive mid-level features to represent local image information and then calculated multi-scale and multi-level saliency maps to obtain the final saliency. The other group uses global image feature in their computationally oriented method. Similar to Zhai and Shan's L-channel Contrast (LC) [22] method, such methods only use the L-channel of an image to compute each pixel's contrast. Hou _et al._[23] proposed Spectral Residual (SR) to obtain the saliency in the frequency domain of an image.