Content-based image retrieval is an important area of research. Here, a method to characterize visual appearance for determining global similarity in images is described. Images are filtered with Gaussian derivatives and geometric features are computed from the filtered images. The geometric features used here are curvature and phase. Two images may be said to be similar if they have similar distributions of such features. Global similarity may, therefore, be deduced by comparing histograms of these features. This allows for rapid retrieval. The system's performance on a database of about 1500 grey-level images and another database of 2000 trademark images is shown. It is also shown that the approach is scalable and examples of query results on a database of more than 63000 trademark images are provided.
This paper describes the evaluation of ARTISAN, a system designed to provide automatic retrieval of abstract trademark images by shape feature. The system depends for its operation on analyzing each image to characterize key shape components, grouping image regions into families which potentially mirror human image perception, and then deriving characteristic indexing features from these families. A variety of run-time search options is provided, allowing the user to select alternative sets of shape features and similarity matching paradigms.The system's retrieval effectiveness has been evaluated by measuring its retrieval effectiveness using a set of 12 real queries with known results put to a collection of over 10 000 abstract geometric shapes from the UK Trade Marks Registry. Normalized recall and precision scores averaged 0.93 and 0.65 respectively. The results suggest strongly that the basic ARTISAN approach is valid, though the present version has significant limitations, particularly when handling badly-scanned images or images with implied shape features. Possible ways of overcoming these limitations are discussed.
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