Image object search combining color with Gabor wavelet shape descriptions

An image and object search and retrieval algorithm is devised that combines color and spatial information. Spatial characteristics are described in terms of Wiskott’s jets formulation, based on a set of Gabor wavelet functions at varying scales, orientations and locations. Color information is first converted to a form more impervious to illumination color change, reduced to 2D, and encoded in a histogram based on a new stretched chromaticity space for which all bins are populated. An image database of 27,380 images is devised by replicating 2,738 JPEG images by a set of transforms that include resizing, various cropping attacks, JPEG quality changes, aspect ratio alteration, and reducing color to grayscale. Correlation of the complete encode vector is used as the similarity measure. For both searches with the original image as probe within the complete dataset, and with the altered images as probes with the original dataset, the grayscale, the stretched, and the resized images had near-perfect results. The most formidable challenge was found to be images that were cropped both horizontally as well as vertically. The algorithm’s ability to identify objects, as opposed to just images, is tested. In searching for images in a set of 4 classifications, the jets were found to contribute most analytic power when objects with distinctive spatial characteristics were the target.