Shape-based image retrieval applied to trademark images

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.

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