Shape Feature Matching for Trademark Image Retrieval

Eakins, John P.; Riley, Kathryn; Edwards, Jonathan

doi:10.1007/3-540-45113-7_4

Cited by 34 publications

(24 citation statements)

References 15 publications

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“…Comparative evaluation experiments carried out in the ARTISAN project [6] seem to clearly indicate that component-based matching of trademark images is significantly more effective than whole-image matching. The same studies, however, have also indicated that the problem of segmentation of multi-component abstract geometric images in a perceptually significant way is still an open problem (see, e.g., Fig.…”

Section: Paper Contributionmentioning

confidence: 92%

“…Principles deriving from Gestalt psychology are at the basis of the ARTISAN project. The first prototype system [10] uses a combination of simple global features computed both from single image components and from families of grouped components; further versions [6] show improvements in the grouping phase, the use of multi-resolution analysis to cope with texture and noise, and the introduction of a variety of shape measures. Similar principles to ARTISAN give foundation to the work of Alwis and Austin [11], which uses perceptual relationships between local features (such as co-linearism, co-curvilinearism, parallelism, and end-point proximity) as well as features computed on closed contours of the images.…”

Section: Literature Reviewmentioning

confidence: 99%

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Retrieval of trademark images by means of size functions

2006

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Section: Paper Contributionmentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Retrieval of trademark images by means of size functions

2006

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“…Jain and Vailaya (1998) proposed the use of the edge direction histogram and improved the descriptor so that it became scale and rotation invariant. Other research includes a comparative study of several common shape-based descriptors for trademark similarity comparison (Eakins et al, 2003) and a compositional shape descriptor that combines several shape descriptors (Hong & Jiang, 2008;Wei et al, 2009).…”

Section: Existing Trademark Search Systemsmentioning

confidence: 99%

Multi-faceted assessment of trademark similarity

Setchi

Anuar

2016

Expert Systems with Applications

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Highlights A novel method for the assessment of trademark similarity is proposed. The method blends together visual, semantic and phonetic similarity. It produces an aggregated score based on the individual assessments. Evaluation using information retrieval measures and human judgment.ACCEPTED MANUSCRIPT One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting.

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“…On the other hand, several approaches based on geometric-based features are proposed for TIR, including Fourier description, curvature scaled space [14], edge directions histograms [15], Triangle-Area Representation (TAR) [16], the local patterns of an image [17], etc. A comparative retrieval study between a region and a geometric-based feature found that the matching of the geometric-based feature was significantly more effective than the matching of the region-based feature [18]. There is consensus that the use of multiple region-based features or the integration of region and geometric-based features work better than any single ones, while integrating various shape descriptors is generally necessary.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System

Chen

Weng

2017

Applied Sciences

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Abstract:The existing trademark image retrieval (TIR) approaches mostly use complex image features, the integration of multi features, a tree structure, etc. to enable highly accurate retrieval. However, there is the heavy computational burden for complex image features and maximum similarity subtree isomorphism (MSSI) measurement. This paper aims to provide an efficient solution for TIR in real-time applications, especially in measuring the similarity between multi-object trademark images. In particular, we propose a novel algorithm for tree similarity measurement based on the fuzzy inference system (FIS) to improve retrieval efficiency. Furthermore, the integration of global and local geometric descriptors is used to enable accurate retrieval. The global descriptor is computed by employing the Hu moments, while the local descriptors are generated by using a tree structure based on the five geometric features: convexity, eccentricity, compactness, circle variance, and elliptic variance. During the retrieval process, the similarity coefficient between the query and the database image is obtained from the similarity of the global and local descriptors. The proposed technique is evaluated using 1800 trademark images, including 12 different classes and 416 trademark images. Additionally, the three common indices, the precision/recall rate, the Bull's eye score, and the average normalized modified retrieval rank (ANMRR) are used as the performance indices. The experimental results show that the proposed technique is superior to the other two competitive approaches. It shows 19.43% and 26.78% precision/recall improvement, 19.56% and 30.58% improvement in the average Bull's eye score, and 0.167 and 0.236 improvement in the ANMRR score, respectively, for the 416 query images. It can be concluded from the experimental analysis that the proposed technique not only provides reliable retrieval results but also improves the retrieval efficiency by 151 times in the retrieval process.

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Shape Feature Matching for Trademark Image Retrieval

Cited by 34 publications

References 15 publications

Retrieval of trademark images by means of size functions

Retrieval of trademark images by means of size functions

Multi-faceted assessment of trademark similarity

An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System

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