  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$_{\beta}$
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.
