Unsupervised Object Extraction by Contour Delineation and Texture Discrimination Based on Oriented Edge Features

This paper presents an unsupervised object extraction system that extracts a single object from natural scenes without relying on color information. The contour information and texture information are analyzed through separate oriented-edge-based processing channels and then combined to complement each other. Contour candidates are extracted from multiresolution edge maps, whereas the local texture information is compactly represented by an oriented-edge-based feature vector and then analyzed by K-means clustering. The object region is determined by merging the results of two separate analysis channels based on the simple assumption that the object is located centrally in the scene. As a result, the object region has been successfully extracted from the scene with a well-defined single boundary line. Both subjective and objective evaluations were carried out and it is shown that the proposed algorithm handles the challenges of complex background well, using only gray-scale images.