Next Article in Journal
FE Model and Operational Modal Analysis of Lower Limbs
Next Article in Special Issue
A Novel Approach to Extract Significant Patterns of Travel Time Intervals of Vehicles from Freeway Gantry Timestamp Sequences
Previous Article in Journal
Irradiation Induced Defect Clustering in Zircaloy-2
Previous Article in Special Issue
Enhancement of Sea Wave Potential Energy with Under-Sea Periodic Structures: A Simulation and Laboratory Study
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(8), 849;

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

Graduate Institute of Automation Technology, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd, Taipei 10608, Taiwan
Author to whom correspondence should be addressed.
Received: 7 July 2017 / Revised: 4 August 2017 / Accepted: 14 August 2017 / Published: 18 August 2017
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2017)
Full-Text   |   PDF [3323 KB, uploaded 18 August 2017]   |  


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. View Full-Text
Keywords: trademark image retrieval; multiple objects; Hu moments; fuzzy inference trademark image retrieval; multiple objects; Hu moments; fuzzy inference

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Chen, C.-S.; Weng, C.-M. An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System. Appl. Sci. 2017, 7, 849.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top