A novel fusion approach to content-based image retrieval

Qi, Xiaojun; Han, Yutao

doi:10.1016/j.patcog.2005.04.005

Cited by 33 publications

(10 citation statements)

References 36 publications

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“…In contrast to partitioning image into grid of blocks, in the region-based systems, the image is segmented into homogenous regions by performing the image segmentation algorithms and then features are extracted from regions [4,7,[14][15][16]. In this approach, the assumption is that the image segmentation algorithm can extract regions that are corresponding to the image objects.…”

Section: Region Extractionmentioning

confidence: 99%

A concept-based model for image retrieval systems

Zarchi

Monadjemi

Jamshidi

2015

Computers & Electrical Engineering

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Section: Region Extractionmentioning

confidence: 99%

A concept-based model for image retrieval systems

Zarchi

Monadjemi

Jamshidi

2015

Computers & Electrical Engineering

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“…The image features are calculated both on image itself and its complement. In [20], to calculate of edge histogram, the four connected sub-images are clustered to generate five different clusters as shown in Fig.2.…”

Section: Image Segmentation For Feature Extractionmentioning

confidence: 99%

The Feature Related Techniques in Content-Based Image Retrieval and Their Application in Solar Image Data

Sun

Deng

Wang

et al. 2013

2013 6th International Conference on Intelligent Networks and Intelligent Systems

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The paper mainly investigates the widely-used image features and related feature selection methods in contentbased image retrieval (CBIR).The texture features and shape features that could be used in the retrieval of solar images are mainly discussed. The result of the survey indicates that although the CBIR related research work in solar image retrieval is comparatively less, the existing achievements still have great instruction for our future work in this domain.

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“…6, it is seen that images are retrieved with fairly satisfactory precision. Some other significant work on image retrieval are available in Reference (111)(112)(113). The authors in Reference (111) propose an image retrieval system using texture similarity, whereas the authors in Reference (112) present a novel information fusion approach for use in content-based image retrieval.…”

Section: Content-based Image Retrieval (Cbir)mentioning

confidence: 99%

Fuzzy Image Processing and Recognition

Pal¹

2007

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article describes various fuzzy set theoretic tools and explores their effectiveness in representing/describing various uncertainties that might occur in an image recognition system and the ways these uncertainties can be managed in making a decision. Some examples of uncertainties that often develop in the process of recognizing a pattern are given in the next section. The Image Ambiguity and Uncertainty Measures Section provides a definition of image and describes various fuzzy set theoretic tools for measuring information on grayness ambiguity and spatial ambiguity in an image. Concepts of bound functions and spect sets charactering the flexible in membership functions are discussed in Their applications to formulate some low level vision operations (e.g., enhancement, segmentation, skeleton extraction, and edge detection), whose outputs are crucial and responsible for the overall performance of a vision system, are then presented. Some real‐life applications (e.g., motion frame analysis, character recognition, remote sensing image analysis, content‐based image retrieval, and brain MR image segmentation) of these methodologies and tools are then described. Finally, conclusions and discussion are provided. Introduction Uncertainties in a Recognition System and Relevance of Fuzzy Set Theory Image Ambiguity and Uncertainty Measures Grayness Ambiguity Measures Flexibility in Membership Functions Some Examples of Fuzzy Image Processing Operations Some Applications Conclusions and Discussion Acknowledgment

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A novel fusion approach to content-based image retrieval

Cited by 33 publications

References 36 publications

A concept-based model for image retrieval systems

A concept-based model for image retrieval systems

The Feature Related Techniques in Content-Based Image Retrieval and Their Application in Solar Image Data

Fuzzy Image Processing and Recognition

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