Abstract
This paper proposes an efficient content-based image feature extraction technique in the DCT domain using histogram quantization. The features are extracted from DCT block coefficients. These coefficients represent the low frequency content of the input image. This method extracts and constructs a feature vector of histogram quantization from partial DCT coefficient in order to count the number of coefficients having the same DCT coefficient over all image blocks. The database image and query image is equally divided into non overlapping 8X8 block pixel Therefore, each of which are associated with a feature vector of histogram quantization derived directly from discrete cosine transform DCT. Users can select any query as the main theme of the query image. The retrieval images is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the Euclidean distance. The experimental results show significant promising results that our approach is easy to identify main objects and try to reduce the influence of background in the image, and thus improve the performance of image classification.