Supervised Learning Methods for Skin Segmentation Based on Pixel Color Classification

Skin segmentation based on multi pixel color clustering models

Supervised Segmentation by Iterated Contextual Pixel Classification

We propose a general iterative contextual pixel classifier for supervised image segmentation. The iterative procedure is statistically well-founded, and can be considered a variation on the iterated conditional modes (ICM) of Besag. Having an initial segmentation, the algorithm iteratively updates it by reclassifying every pixel, based on the original features and, additionally, contextual info...

A comparison of supervised pixel-based color image segmentation methods. Application in cancerology

In this paper, we describe a new scheme to color image segmentation which is based on supervised pixel classification methods. Using color pixel classification alone does not extract accurately enough color regions, so we suggest to use a strategy based on four steps in different color spaces: simplification, pixel classification, marker extraction and color watershed growing. The strategy is p...

Modified CLPSO-based fuzzy classification System: Color Image Segmentation

Fuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by selecting a fuzzy classification system with minimum number of fuzzy rules and minimum number of incorr...

Pixel-Based Skin Detection for Pornography Filtering

A robust skin detector is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography filtering. Less than 10 years ago, the first paper on automatic pornography filtering was published. Since then, different researchers claim different color spaces to be the best choice for skin detection in pornography filtering. Unfortunately, no com...