## 1 Introduction  
Saliency detection has continuously been one of the focuses of research in the computer vision field. As indicated in recent applications in image segmentation [1], object detection [2], image retrieval based on content [3], image classification [4], image semantic understanding [5], _etc._, progress on saliency detection research is one of the principal factors leading to performance improvement of work in the relevant field. In general, saliency detection should meet the following criteria: (1) The salient object/region detected needs to be accurately located in images, which is ideally coherent with humans perceiving focus of region/object in cluster scenes. (2) The salient object/region detected needs to be clearly distinguished from complex background, while, ideally, retaining the information integrity of the object/region, such as inherent complete contour and local texture details as much as possible to facilitate successive image processing and analysis. (3) Saliency detection should be performed within an acceptable timescale, and the overhead of the computation involved should be low [6] (Figure 1). In accordance with the human vision attention mechanism, visual saliency of an image is defined as how much a certain region/object in an image visually stands out from its surrounding area with high contrast. To detect the visual saliency as defined, there are many contrast-based methods recently reported in the literature, such as Frequency-Turned (FT) [7], Histogram-based Contrast (HC) [8, 9], Region-based Contrast (RC) [8, 9], SF (Saliency Filters) [10], and FASA (Fast, Accurate, and Size-Aware) [6]. However, FT [7] method directly defines each pixel's color difference from the average  
Figure 1: An example comparison of original images, salient maps and ground truth; from top to bottom: original images, salient maps and ground truth. color value of the whole image as the pixel's contrast, therefore, the saliency detected in this method is sensitive to image edges and noise, and the performance of the detection is poor. Cheng _et al_. proposed HC and RC in [8,9]. Because both of the methods only use the color feature, saliency detection performed in these two methods often achieves a blurry salient map when working with cluttered images. Furthermore, SF [10] method, which is also based on contrast, combines global contrast and spatial relations together to generate the final salient map. However, in some cases, SF [10] method cannot differentiate the salient region/object with sufficiently complete contours and detailed local texture from a complex background. Although it is superior to the others in terms of speed, FASA [6] sometimes falsely marks the background as the salient region because the detection is also carried out using solely the color feature for calculating the global color contrast and saliency probability. Examples of saliency maps generated by FT [7], HC [8,9], RC [8,9], SF [10] and FASA [6] methods are shown in Figure 2, in which the defects of incomplete contour, blurred local texture details and the false marking of background as salient region can be visually identified in corresponding maps. The figurative result in Figure 2 reveals by analysis that the reason the defects are induced lies in the fact that only information used in the detection process is the color feature, which is, apparently, insufficient. In contrast to the global color information, the local texture information, as one of the dominant visual features in the real world, presents important information about the smoothness, coarseness and regularity of an object's local details. Therefore, it would be better to combine the local texture feature and the color feature together to enrich the information used in saliency detection, and hopefully improve the accuracy of salient region/object location and retaining the inherent information that the detected salient region/object possess. There are many descriptors available for local region texture description.