A method for extracting grouping areas of good continuity parts in trademark images

Authors:
Brice Befane;Koji Abe;Takahiro Hayashi
Affiliations:
Kinki University, Osaka, Japan;Kinki University, Osaka, Japan;Niigata University, Niigata, Japan
Venue:
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Year:
2012

Citing 3
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Similarity Retrieval of Trademark Images

IEEE MultiMedia
Recognition of Plural Grouping Patterns in Trademarks for CBIR According to the Gestalt Psychology*This research project was partially supported by the JSPS Grants-in-Aid for Scientific Research (Young Scientist B, no. 16700117) and the preliminary version of this paper was presented at the 7th IASTED International Conference on Human-Computer Interaction held on Nov. 14--16, at Phoenix, Arizona, USA, 2005.

IEICE - Transactions on Information and Systems

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Abstract

According to the Gestalt principles, this paper presents a method for recognizing grouping areas of good continuity in trademark images. The purpose of this investigation is to enhance performances of content-based image retrieval by mirroring human perception. On recognizing grouping areas on good continuity, the physical factors suggested by the Gestalt Psychology are: proximity between couples of components in a set of components in one image and shape similarity between these components. Considering the factors, the proposed method measures proximity between every couple of components and judges whether the couple could be a part of the grouping area using a support vector machine. Then, the proposed method extracts features on shape similarity from every component and measures difference of the features between every couple of components. After then, using the difference, the couple is judged as well as the case of proximity. The learning data in the SVM are determined by results of questionnaire on good continuity for 75 sample images. To examine performance of the proposed method, another 75 trademark images have been tried and experimental results have shown correspondence ratios between grouping patterns output by the proposed method and results of the questionnaire have been more than 85.41%.