A Computational Model for Segmenting color images, Color Naming and Describing Color Composition of Images
Dr.G.K.Viju, Professor, Department of Computer Science,
Karary University, POBox-12304, Khartoum, Sudan
Mobile: +249 919471821
This paper presents a computational model for color categorization, naming and extraction of Color composition from color images. The main objective is to segment a color image using spatial averaging segmentation. And attach color names to the segmented regions. Finally, the algorithm is extended to develop a scheme for extracting the color composition of a complex image. For example, regions labeled as light blue and strong green may represent sky can be easily identified. Using color names to label regions can often improve segmentation result. In many cases, color names only, or in combination with other features can help enhance information about analyzed images and reveal their semantics
Color is one of the main visual cues and has been studied extensively on many different levels, starting from the physics and psychophysics of color to the use of color principles in practical problems, such as accurate rendering, display and reproduction, segmentation, and numerous other applications in image processing, visualization and computer graphics. Although color naming represents one of the most common visual tasks, it has not received significant attention in the engineering community. Yet today, with rapidly emerging visual technologies, sophisticated user interfaces and human-machine interactions, the ability to name individual colors, point objects of certain color, and convey the impression of color composition becomes an increasingly important task. The extraction of higher-level color descriptors represents a challenging problem in image analysis and computer vision, as
these descriptors often provide link to image...