Unsupervised colour image segmentation by low-level perceptual grouping

Abstract

This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

Joint Color-Spatial-Directional Clustering and Region Merging (JCSD-RM) for Unsupervised RGB-D Image Segmentation

“…Image segmentation based on region merging is one of the oldest techniques used in computer vision [2]. Numerous existing methods which merge regions in a RGB image exploit color and edge information [3], [31], [32], [33]. For indoor scenes, the use of color is often unreliable due to numerous effects caused by spatially varying illumination [6] and the presence of shadows.…”

Segmentation for Image Indexing and Retrieval on Discrete Cosines Domain

“…But one of the weaknesses of this method is that, it ignores the spatial relationship information of the pixels. Adolfo [4] proposed a neural network based adaptive thresholding segmentation algorithm for monochrome image.…”

“…A recent article published by Smeulders [5], reviewed more than 200 references in this ever changing field. Readers are referred to that article and some additional references [4] for more information. Most of the CBIR projects aimed at general-purpose image indexing and retrieval systems focus on searching images visually similar to the query image or a query sketch.…”