Automatic image annotation using synthesis of complementary features

Image annotation is the process of assigning meaningful keywords to an image. In automatic image annotation, this process is executed automatically by checking the semantics of the image. The semantics contained in an image is interpreted by extracting some high level and low level image features. This work implements a system that automatically annotates colour images using a special feature extraction mechanism, which can be very effectively used for image sequence recognition or classification. This special feature is driven out by combining, three features under research, namely, Histogram of Oriented Gradients (HOG), Speeded Up Robust Features (SURF) and a color feature based on HSV Colour structure. Thus, we formed a synthesized feature descriptor which essentially describes three aspects visual perception, the colour, shape, and points of interest. The proposed system follows a hybrid approach which first trains a specific set of data and annotation is performed using fuzzy K-NN classification. In our experiment, it has been observed that the system has good accuracy and high potential in textual description of digital photographic images.