A Novel Similarity Measure using a Normalized Hausdorff Distance for Trademarks Retrieval

نویسنده

  • Bei-ji Zou
چکیده

In this paper we provide a novel measure based on direct Hausdorff distance (DHD). Most researchers have used the Euclidean distance (EUD) or DHD. We propose the use of normalized cosine distance (COSD) and EUD as finite set points instead of a set of image pixels. The proposed measure takes into account the integration of global and local features. For the performance assessment a genetic algorithm (GA) is applied to decide the best weight factors distribution. We have also used the retrieval efficiency equation in order to test the accuracy of the method. The obtained result showed that normalized Hausdorff distance (NHD) provides a significant improvement in retrieval accuracy and is robust against shape invariant transformations. Moreover, our shape retrieval algorithm proves to be efficient, promising and satisfies the human perception quite well.

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تاریخ انتشار 2010