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A content-based image retrieval system
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DOI：10.1016/S0262-8856(97)00062-0.png)
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Abstract
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This paper proposes a Content-Based Images Retrieval (CBIR) system which uses a modified geometric hashing technique to retrieve similar shape images from the image database. The CBIR system is a two-stage image retrieval system: the outline-based image retrieval and the hash-table-based image retrieval. For each object, we extract the feature points to generate the individual hash-table which is constructed by using the geometric properties of every three feature points. In the first retrieval stage, we use the shape parameters of the input sketched query image to select the possible candidate models in the database. The individual hash tables of these candidate models are combined as the global hash table for the second retrieval stage which is a voting process using the invariant indices from the sketched query image and the global hash table. The number of votes indicates the score of matching between the query image and the candidate models. In the experiments, we have illustrated that the CBIR system can accurately retrieve the similar images from the database by using scaled, rotated, or mirrored sketched query images. (C) 1998 Elsevier Science B.V.
Keywords:
geometric hashing
Fourier descriptor
invariant moment
feature point selection
similarity measure
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