Article

SRUJY KRISHNA A U, FEDERAL INSTITUTE OF SCIENCE AND TECHNOLOGY, ANGAMALY, ERNAKULAM, KERALA, INDIA; SHIMY JOSEPH ,FEDERAL INSTITUTE OF SCIENCE AND TECHNOLOGY, KERALA, INDIA

The objective of salient region detection is to separate salient region from entire image. This salient region detection framework consists of a structure-guided statistical textural distinctiveness approach. This approach includes the five main stages: i) image decomposition ii) textural representation, iii) texture modeling, iv) matrix construction, and v) saliency map computation. In the image decomposition stage, decomposition of image into structural image elements to learn a structure-guided texture model. In second stage, define a rotational-invariant neighborhood based texture feature model that represents the underlying textural characteristics of regions in a local manner. In texture modeling stage, Sparse texture modeling is done using structure-guided texture learning. In matrix construction stage, characterize all pair-wise statistical textural distinctiveness within the sparse texture model of the image and construct a textural distinctiveness matrix. In the final stage, the saliency of a region can be computed as the expected statistical textural distinctiveness of the region in the given image .The proposed approach has been extensively evaluated on images from MSRA-1000 datasets.