Extraction and Recognition of Color Feature in true Color Images Using Neural Network Based on Colored Histogram Technique

• Main Author: Orjuwan Aljawadi
• Format: Article
• Language: Arabic
• Published: Mosul University 2012-12-01
• Series: Al-Rafidain Journal of Computer Sciences and Mathematics
• Subjects: neural networks; cone cells; algorithms
• Online Access: https://csmj.mosuljournals.com/article_163709_892165026eea3b60d4a6e887073137ee.pdf
• ISSN: 1815-4816; 2311-7990

In this research, a neural network using backpropagation (BPNN) algorithm was trained and learned to work as the cone cells in human eyes to recognize the three fundamental cells’ colors and hues, as the neural network showed good results in training and testing the color feature  it was trained and learned again to recognize two nature scenes images ; Red sunset and Blue sky images where both scenes images contain color interaction and different hues such as red-orange and blue-violet. The recognition process was based on color histogram technique in colored images which is a representation of the distribution of colors in an image by counting the number of pixels that have colors in each of a fixed list of color ranges, that span the image's color space , all possible colors in the image. The importance of this research is based on developing the ability of (BPNN) in images ‘objects recognition based on color feature that is very important feature in artificial intelligence and colored image processing fields from developing the systems of alarms robots in fire recognition , medical digenesis of tumors, certain pattern’s recognition in different segments of an image , face and eyes’ iris recognition as a part of security systems , it helps solve the problem of limitation of recognition process in neural networks in many fields.