Image Object Search Combining Colour with Gabor Wavelet Shape Descriptors

An image and object search and retrieval algorithm is devised that combines colour 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. Colour information is first converted to a form more impervious to illumination colour change, reduced to 2D, and encoded in a histogram. The histogram, which is based on a new stretched chromaticity space for which all bins are populated, is resized and compressed by way of a DCT. An image database is devised by replicating JPEG images by a set of transforms that include resizing, various cropping attacks, JPEG quality changes, aspect ratio alteration, and reducing colour to greyscale. 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 also tested. In searching for images in a set of 5 classifications, the jets were found to contribute most analytic power when objects with distinctive spatial characteristics were the target.