Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Digital Image Processing
Similarity Retrieval of Trademark Images
IEEE MultiMedia
Resiliency and Robustness of Alternative Shape-Based Image Retrieval
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
Improved Stochastic Modeling of Shapes for Content-Based Image Retrieval
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
IRUS: Image Retrieval Using Shape
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
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In this chapter, we propose a new shape-based, query-by-example, image database retrieval method that is able to match a query image to one of the images in the database, based on a whole or partial match. The proposed method has two key components: the architecture of the retrieval and the features used. Both play a role in the overall retrieval efficacy. The proposed architecture is based on the analysis of connected components and holes in the query and database images. The features we propose to use are geometric in nature, and are invariant to translation, rotation, and scale. Each of the suggested three features is not new per se, but combining them to produce a compact and efficient feature vector is. We use hand-sketched, rotated, and scaled, query images to test the proposed method using a database of 500 logo images. We compare the performance of the suggested features with the performance of the moments invariants (a set of commonly-used shape features). The suggested features match the moments invariants in rotated and scaled queries and consistently surpass them in handsketched queries. Moreover, results clearly show that the proposed architecture significantly increase the performance of the two feature sets.