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The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. Most models in the literature that use the color and texture features treat them separately. In our case, it is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by vector whose components are from a superpixel obtained by SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-texture is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling), that considers the color micro-textures non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB, HSL, LUV and CMY color spaces. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error) and F$_β$ measures of our saliency maps, on the complex ECSSD dataset show that our model is both simple and efficient, outperforming several state-of-the-art models.