The idea proposed in this paper utilizes an efficient image retrieval system that is based on computation and combination of grey level, shape and texture features. The proposed system calculates various grey level features which are used to extract attributes from an input image. Grey Level Co-occurrence Matrix is used to compute the texture feature of a given image while the Shape feature fin...

These approaches utilize different features for face recognition purpose. The feature utilized for face recognition are shape, distance between two traits of face, texture features for face. Texture features are particularly susceptible to the resolution of images, when the resolution changes the calculated textures are not accurate. Texture features computed for low resolution face images does...

Assets have different shapes, colors and textures. A shape feature is the main characteristic, which distinguishes each type of asset in addition to the other features, i.e. the color and the texture. The value of features in the image-based asset information retrieval is used as the key field by comparing the similarities between the images. These similarities can be determined based on the di...

Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is e...

This paper proposes an image retrieval method based on multi-feature similarity score fusion using genetic algorithm. In recent years, research and development in the content-based image retrieval has mainly focused n image features, such as color, shape, texture and spatial relationships. In addition, many content-based image retrieval systems and methods have been developed for various applic...

In this paper, Focus is on texture as primary feature. Shape and spatial information were secondary features. Texture features derived from nine grid sizes of independent and different Gabor filter banks were incorporated into the CBIR system by taking advantage of the fact that each grid size of filter is suited to capture particular set of localized frequency-images in diverse database. This ...