Salient object detection employing regional principal color and texture cues

Abstract

Saliency in a scene describes those facets of any stimulus that makes it stand out from the masses. Saliency detection has attracted numerous algorithms in recent past and proved to be an important aspect in object recognition, image compression, classification and retrieval tasks. The present method makes two complementary saliency maps namely color and texture. The method employs superpixel segmentation using Simple Linear Iterative Clustering (SLIC). The tiny regions obtained are further clustered on the basis of homogeneity using DBSCAN. The method also employs two levels of quantization of color that makes the saliency computation easier. Basically, it is an adaptation to the property of the human visual system by which it discards the less frequent colors in detecting the salient objects. Furthermore, color saliency map is computed using the center surround principle. For texture saliency map, Gabor filter is employed as it is proved to be one of the appropriate mechanisms for texture characterization. Finally, the color and texture saliency maps are combined in a non-linear manner to obtain the final saliency map. The experimental results along with the performance measures have established the efficacy of the proposed method.