A Novel Vector-Based Approach to Color Image Retrieval Using a Vector Angular-Based Distance Measure

Abstract

Color is the characteristic which is most used for image indexing and retrieval. Due to its simplicity, the color histogram remains the most commonly used method for this task. However, the lack of good perceptual histogram similarity measures, the global color content of histograms, and the erroneous retrieval results due to gamma nonlinearity, call for improved methods. We present a new scheme which implements a recursive HSV-space segmentation technique to identify perceptually prominent color areas. The average color vector of these extracted areas are then used to build the image indices, requiring very little storage. Our retrieval is performed by implementing a combination distance measure, based on the vector angle between two vectors. Our system provides accurate retrieval results and high retrieval rate. It allows for queries based on single or multiple colors and, in addition, it allows for certain colors to be excluded in the query. This flexibility is due to our distance measure and the multidimensional query space in which the retrieval ranking of the database images is determined. Furthermore, our scheme proves to be very resistant to gamma nonlinearity providing robust retrieval results for a wide range of gamma nonlinearity values, which proves to be of great importance since, in general, the image acquisition source is unknown.

Towards Face Recognition Using Eigenface

“…Hue is the feature of visual impression that relates to color sensitivity linked with the prevailing colors, saturation infers the relative purity of the color component and value indicates the brightness of a color. The conversion from RGB space to HSV space is expressed by the equations [20][21][22]: To locate the face, an image pyramid is built from a set of facial images with different scales and resolutions. For this, a face template is moved from left to right and up to bottom over each image in the pyramid and calculate the matching probability at each position of the image segment under the template using minimum Manhattan distance.…”

Content -based Image Retrieval for Image Indexing

“…The HSV color model organizes similar colors under similar hue alignments. The transformation from RGB color space to HSV space is given by the equations [23,[26][27][28].…”

“…McKenna, Raja and Gong [22] employed an adaptive Gaussian mixtures to model the color allocations of objects. Androutsos, Plataniotis and Venetsanopoulos [23] established a cosine metric based distance measure for color image indexing and retrieval. Their query method is very flexible and provides single and multiple color queries.…”

A New Inpainting Method for Highlights Elimination by Colour Morphology

“…Androutsos et al in [9] make a division of the luminance-saturation space (HLS) and they conclude that if the saturation is greater than a 20% and the luminance is greater than a 75%, the pixels are chromatic, while if the saturation is lower than a 20% and the luminance is greater than 75%, the pixels are very luminous or highlights. Our criterion is similar and it is based, initially, on the division of the value-saturation space in different homogenous regions that segment the chromatic image.…”