Color, texture and shape descriptor fusion with Bayesian network classifier for automatic image annotation

Due to the large amounts of multimedia data prevalent on the Web, Some images presents textural motifs while others may be recognized with colors or shapes of their content. The use of descriptors based on one’s features extraction method, such as color or texture or shape, for automatic image annotation are not efficient in some situations or in absence of the chosen type. The proposed approach is to use a fusion of some efficient color, texture and shape descriptors with Bayesian networks classifier to allow automatic annotation of different image types. This document provides an automatic image annotation that merges some descriptors in a parallel manner to have a vector that represents the various types of image characteristics. This allows increasing the rate and accuracy of the annotation system. The Texture, color histograms, and Legendre moments, are used and merged respectively together in parallel as color, texture and shape features extraction methods, with Bayesian network classifier, to annotate the image content with the appropriate keywords. The accuracy of the proposed approach is supported by the good experimental results obtained from ETH-80 databases. Keywords—image annotation; k-means segmentation; Bayesian networks; color histograms; Legendre moments; Texture; ETH-80 database