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This study presents a novel and highly efficient superpixel algorithm, namely, depth-fused adaptive superpixel (DFASP), which can generate accurate superpixels in a degraded image. In many applications, particularly in actual scenes, vision degradation, such as motion blur, overexposure, and underexposure, often occurs. Well-known color-based superpixel algorithms are incapable of producing accurate superpixels in degraded images because of the ambiguity of color information caused by vision degradation. To eliminate this ambiguity, we use depth and color information to generate superpixels. We map the depth and color information to a high-dimensional feature space. Then, we develop a fast multilevel clustering algorithm to produce superpixels. Furthermore, we design an adaptive mechanism to adjust the color and depth information automatically during pixel clustering. Experimental results demonstrate that regardless of boundary recall, under segmentation error, run time, or achievable segmentation accuracy, DFASP is better than state-of-the-art superpixel methods.

The independency between two attribute subsets can be verified based on Chi square statistic to reduce candidate sets. Based on this measure, heuristic algorithm employing information entropy for reduction of decision systems is presented by combining rough sets and statistics. And the validity of this algorithm is analyzed.