@inproceedings {368, title = {Robust invariant descriptor for symbol-based image recognition and retrieval}, booktitle = {1st IEEE International Conference on Semantic Computing}, year = {2007}, abstract = {

This paper presents a robust invariant descriptor for symbol-based image recognition and retrieval. A modified Hough-based Transform is used to extract parameter space information (i.e., position data and angular data) from a symbol image to derive an invariant descriptor. The proposed descriptor provides a compact representation of a symbol image that can be evaluated efficiently. The extracted descriptor is highly robust against geometric transformations such as translation, rotation, reflection, and scaling, and image degradation. A series of experiments were conducted using a set of architectural and engineering symbols subjected to geometric transformations and image degradation. The experimental results clearly show that the proposed descriptor can be used effectively for symbol recognition and retrieval.

}, keywords = {content-based retrieval, Feature Extraction, geometric transformation, Hough transforms, Hough-based transform, image degradation, image recognition, image representation, image retrieval, robust invariant descriptor, symbol-based image recognition}, doi = {http://dx.doi.org/10.1109/ICSC.2007.24}, author = {A Wong and W Bishop} } @inproceedings {648, title = {Phase-based methods for Fourier shape matching}, booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2004}, address = {Montreal}, abstract = {

Interest in the problem of shape matching has been recently reignited by the huge proliferation of images on the Internet, particularly huge computer databases containing thousands or millions of images. Although many typical image database algorithms rely on a variety of shading, texture, and colour attributes, there are significant opportunities for the use of shape as a discriminator, particularly for binary or high-contrast images. Fourier shape descriptors have been studied extensively for shape comparison, however the descriptor phases have been mostly neglected - either ignored entirely or treated simplistically. This paper formalizes the use of phase in shape matching, and derives shape discriminators, applicable to both simple and complex shapes.

}, keywords = {binary images, complex shapes, fast Fourier transforms, Fourier shape descriptors, Fourier shape matching, high-contrast images, huge computer databases, image database algorithms, image matching, image retrieval, Internet, phase-based methods, shape discriminators, simple shapes, very large databases, visual databases}, doi = {http://dx.doi.org/10.1109/ICASSP.2004.1326572}, author = {P Fieguth} } @inproceedings {501, title = {Fast retrieval methods for images with significant variations}, booktitle = {International Conference on Image Processing}, year = {2000}, abstract = {

Fast image retrieval is the key to success for operations on large image databases, and a great many techniques have been developed for efficient retrieval. However, most of these methods are tailored to visual scenes or to images having limited variations. We investigate the searching of enormous databases (of up to 10 7 images) for the matching and identification of precious stones (principally diamonds). Because of the size of the database, we propose a hierarchy of classifiers, which successively prune candidate images such that the more complex classifiers are required to test only tiny portions of the data. The new classifier developed here applies a wavelet transform to image histograms and is capable of rejecting 99.9\% of bad matches.

}, keywords = {classifiers hierarchy, database searching, diamond, diamonds, fast image retrieval methods, image classification, image histograms, image matching, image retrieval, large image databases, precious stones identification, precious stones matching, visual databases, visual scenes, wavelet transform, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2000.899470}, author = {P Fieguth and R Wan} }