IRTF: Image Retrieval through Fuzzy Modeling

Lakdashti, Abolfazl; Moin, Mohammad Shahram; Badie, Kambiz

doi:10.1109/icc.2008.97

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…The precision and recall rates are presented as: precision = n sl /n rt recall = n sl /n rq (28) where n sl is the total number of relevant retrieved images, n rt is the total number of retrieved images, and n rq is the total number of relevant images in the entire database.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Therefore, we illustrate the comparison of the performance of the ranking capability of our technique with that of the competitive approaches. Here, the ranking capability is computed by the precision, and we set that rt n as half of the number of classes in Equation (28). The largest precision represents the best retrieved result.…”

Section: Performance Of Bull's Eye Score and The Retrieval Rankingmentioning

confidence: 99%

“…It relies on finding an end-to-end mapping directly from the raw input to the required output, whereby the best representation for the problem at hand is obtained from the data directly. In the literature [2,[26][27][28][29][30], fuzzy theory is employed to deal with the vagueness and ambiguity of human judgment in terms of image similarity. It provides flexible and fuzzy mapping from low-level features to high-level human concepts.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Experiments Setupmentioning

confidence: 99%

Section: Performance Of Bull's Eye Score and The Retrieval Rankingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Weng

2017

Applied Sciences

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“…It does not classify images and hence does not suffer from mistakes arisen from misclassifying query images. Moreover, it does not require learning semantic rules as is done in the first category in human-based methods, but the rules are extracted automatically [2], [8], [9]. There are some related studies that incorporate fuzzy logic into CBIR systems.…”

Section: Literature Surveymentioning

confidence: 99%

“…In the literature, it is known that fuzzy logic can provide a flexible and vague mapping from low-level numerical features to high-level human concepts [8], [9]. Fuzzy logic deals with reasoning that is approximate rather than fixed and exact.…”

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confidence: 99%

Observations on Using Type-2 Fuzzy Logic for Reducing Semantic Gap in Content–Based Image Retrieval System

Darwish¹,

Ali²

2014

IJCTE

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Abstract-Semantic-based image retrieval has been one of the most challenging problems in recent years. Although so many solutions are provided for filling the so-called gap between the content based image retrieval (CBIR) and what human beings expect from the retrieval task; none of them yields satisfactory results and the problem is still open for further research. In this paper, type-2 fuzzy logic (T2FL) framework is considered to alleviate two problems in traditional CBIR systems, including the semantic gap and the perception subjectivity. Employing T2FL has the potential to overcome the limitations of type-1 fuzzy logic and produce a new generation of fuzzy controllers with improved performance for many CBIR applications that require handling high levels of uncertainty. Thus, our contributions in this study are threefold.(1) The proposed system maps low-level visual statistical features to high-level semantic concepts; enabling to retrieve and browse image collections by their high-level semantic concepts. (2) Type2 fuzzy logic has been used to fuse (combine) extracted features as well as to deal with the ambiguity of human judgment of image similarity. (3) The system models the human perception subjectivity with the ability to handle high levels of uncertainties appropriately. A comparative study with the state-of-the-art type-1 fuzzy based image retrieval approaches reveals the effectiveness of the proposed system. Index Terms-Type-2 fuzzy logic, semantic-based image retrieval, soft computing, image processing.

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IRTF: Image Retrieval through Fuzzy Modeling

Cited by 2 publications

References 9 publications

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

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

Observations on Using Type-2 Fuzzy Logic for Reducing Semantic Gap in Content–Based Image Retrieval System

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