# An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System
###### 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. t 1 2017 7 849 2017 2017 2017 2017 2017 2017 2017 2  
systems is to limit the number of trademarks in order to reduce the time of manual verification. Furthermore, a trademark retrieval system, to be practical, has to be able to work in real time with a good precision/recall curve (a minimum number of irrelevant trademarks are shown to the officer) to ensure its usefulness. The success of a TIR system is dependent upon the method of feature extraction and feature similarity measure. Searches for similar trademarks include a visual and percipient assessment. Visual similarity in the trademark retrieval problem is usually considered through the similarity of content features such as shape, color, or texture [3]. In [6], psychology studies indicate that humans are more prone to identify and distinguish objects based on their shape rather than color and texture. In addition, in [7], the authors conclude that humans can easily discriminate shapes by their contours, and the contour of a shape has importance, not its interior content. Therefore, the technique of this paper is focused on the shape of trademarks. Broadly, shape defines the contour as well as the whole area of an image, and it can provide a greater robustness property for finding the relevant image in the database. The shape feature can be further categorized into two groups: region and geometric based [8; 9; 10]. In the region-based feature, the image moment was widely used in the TIR system, which represents the mass distribution of objects in space. Low-order moments cannot describe the shape accurately; hence, high-order moments are desirable but are more prone to noise. There are different region-based descriptors like Hu's moment (HM), generic Fourier descriptor (GFD), Legendre moments (LMs), Zernike moments (ZMs) [11; 12; 13], etc. ZMs have certain desirable properties like rotation invariance and robustness to noise and also have better retrieval performance than the other region-based descriptors [8].