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A Novel Local Global Specialized Descriptor for Feature Detection in Content Based Image Retrieval

G. Mareeswari, S. Vaishnavi

A new image feature detector and descriptor, namely Local-Global Specialized Descriptor is used for Content Based Image Retrieval (CBIR). In the real world environment the images is embedded with noise that will affect the CBIR algorithms. Some filtering algorithm are applicable for noise reduction, many of the filtering algorithm that is sensitive to one type of noise in an image which has not consider another type of noise that lead to unfavourable results. This lead to the need of designing an efficient CBIR algorithm that retains precision rates even under noisy conditions. In this work, number of experiment has been conducted to analyse the robustness of proposed Combined Global – Local Specialized Features Descriptor (CGLSFD). The proposed methods consist of two stages. In the first stage apply wavelet to decompose the query image to extract the energy, standard deviation and mean values in all bands. In the second stage apply micro structure descriptor (MSD) to extract image edge orientation features with color, texture and shape and color layout information. This proposed method extensively tested on Corel data tests, and this algorithm has high indexing and low dimensionality, also along with other existing conventional algorithms under different types of noises such as Gaussian noise, salt and pepper noise and quantization noises. So this proposed algorithm is robust compare to the existing algorithms.