  Connected component analysis (CCA) has been heavily used to label binary
images and classify segments. However, it has not been well-exploited to
segment multi-valued natural images. This work proposes a novel multi-value
segmentation algorithm that utilizes CCA to segment color images. A user
defined distance measure is incorporated in the proposed modified CCA to
identify and segment similar image regions. The raw output of the algorithm
consists of distinctly labelled segmented regions. The proposed algorithm has a
unique design architecture that provides several benefits: 1) it can be used to
segment any multi-channel multi-valued image; 2) the distance
measure/segmentation criteria can be application-specific and 3) an absolute
linear-time implementation allows easy extension for real-time video
segmentation. Experimental demonstrations of the aforesaid benefits are
presented along with the comparison results on multiple datasets with current
benchmark algorithms. A number of possible application areas are also
identified and results on real-time video segmentation has been presented to
show the promise of the proposed method.
