A New Colour-Texture Feature Extraction Method for Image Retrieval System Using Gray Level Co-occurrence Matrix

Proposed a new colour-texture feature extraction method is presented for Content Based Image Retrieval (CBIR) system using Gray Level Co-occurrence Matrix (GLCM). In this method, Colour-GLCM  (C-GLCM) is extracted from each colour channel, and then computes the average of each column of GLCM matrix for each channel. In this case, we will get a feature vector include colour and texture features at the same time to achieve the objectives of any CBIR system which are; decrease the Feature Vector (FV) dimensions which consequently reduces retrieval time, and also increase the retrieval accuracy.  To perform the evaluation of the proposed CBIR system, 4000 test images have been used as query images including 500 original images were selected randomly from image database of Iraqi National Museum of Modern Art, then applying seven image transformations on each original image resulting 3500 transformations image sued as query image. The proposed C-GLCM algorithm has led to improve and increase the retrieval accuracy (93.63%) comparing with GLCM that extraction from whole gray image (87.88%) and comparing with statistical properties that extraction from GLCM feature (80%)

Adaptive Technique for Contrast Enhancement of Leading Vehicle Tracks

During movement in various unpaved terrain conditions, the track impressions left over by the leading vehicles provide guiding and safe routes in the area. The delineation of these tracks captured by the images can extend immense support for guidance in real time. These tracks that look like edges in coarse-resolution images take the shape of elongated areas in fine-resolution images. In such a scenario, the high pass and edge detection filters give limited information to delineate these tracks passing through different surroundings. However, the distinct texture of these tracks assists in the delineation of these tracks from their surroundings. Gray level co-occurrence matrix (GLCM) representing the spatial relation of pixels is employed here to define the texture. The authors investigated the influence of different resolutions on the distinguishability of these tracks. The study revealed that texture plays an increasing role in distinguishing objects as the image resolution improves. The texture analysis extended to investigate the track impressions left over by the leading vehicle brings out an ample scope in delineating these tracks. The measures could improve the track contrast even better than conventional techniques. To select the most optimal contrast enhancement measure in a given scenario, authors proposed a quantified measure of track index. An investigation is made on the difference-based track index (TI) representing the mean contrast value of the track vis-à-vis off-track areas. The results show an increase in the quantified contrast from 7.83 per cent to 29.06 per cent. The proposed technique highlights the image with the highest track contrast in a given scenario. The study can lead to onboard decision-making for the rut following vehicles moving in low-contrast terrain