improving cbir using feature extraction based on wavelet transform
The "gap semantic" and the "curse of dimensionality" are two shortcomings of content-based image retrieval techniques that rely on automatic feature extracted from images to process similarity queries. The first one represents the semantic gap that exists between low-level features automatically extracted by a computational system, and the high-level user interpretation of images. The second one involves problems occurring when similarity is defined over high-dimensional feature spaces. This paper shows a method that deals with these both shortcomings. We use discrete wavelet transforms to obtain the image representation from a multiresolution point of view. The feature vectors were composed of the features from the approximation subspace, which succinctly represent the images in the processing of similarity queries. In addition, the multiresolution method was used to reduce the dimensionality of the feature space. This work shows the evaluation of three different image datasets, where the first two are composed of medical images and the third one is a generic image dataset. The results are promising and show an improvement of up to 90\% for recall values up to 65\%, in the query results using the Daubechies wavelet transform.
cbir wavelets multiresolution feature vector image processing