Content-Based Image Retrieval Research

Duan, Guoyong; Yang, Jing; Yang, Yilong

doi:10.1016/j.phpro.2011.11.073

Cited by 40 publications

(19 citation statements)

References 5 publications

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“…Guoyong Duan [1] , this paper shows the advantages of the content based image retrieval system, as well as technologies. Also introduces all the feature extraction methods such as color, texture shape for image retrieval with its advantage.…”

Section: Literature Surveymentioning

confidence: 99%

Two Tier Approach for Automatic Retrieval of MRI Brain Image by Feature Extraction

Chavan¹,

Gore²

2015

IJCA

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Section: Literature Surveymentioning

confidence: 99%

Two Tier Approach for Automatic Retrieval of MRI Brain Image by Feature Extraction

Chavan¹,

Gore²

2015

IJCA

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“…Different procedures are used to extract the images. (Guoyong et al, 2011)Texture features like energy, entropy, contrast and uniformity are extracted by means of gray-level co-occurrence matrix (GLCM). Dominant color can be extracted in the RGB color space.…”

Section: (A) Feature Extractionmentioning

confidence: 99%

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

2016

Rev. Téc. Ing. Univ. Zulia

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Content Based Image Retrieval is an active way of searching, browsing, or retrieving images from a large image repository. Researchers are intensely competing for developing efficient and precise image retrieval tool which can be applied to various image searching devices that run on different platforms. By applying static clustering, similarity between query image and each cluster is determined to find the closest cluster which has greater likelihood. To obtain more relevant images, it searches not only a single cluster but also the nearby clusters and when the database size increases, all the clusters get updated to reflect the changes in the database. To overcome this problem the proposed method develops an efficient CBIR system that uses rule based Boolean query. It does not generate clusters, instead a dynamic rule has been derived using query image features to retrieve the primary collection of relevant images which has higher semantics. Instead of using dissimilarity distances directly this system determines the subset of images and then it is refined to yield the topmost closest images by finding the shortest distance using Euclidean distance. Gray-level co-occurrence matrix is used to determine texture features such as energy, entropy, contrast and homogeneity. Region based method is used for shape features such as area, perimeter, solidity and circularity of a largest connected component. Dominant color descriptors in RGB space are used for extracting the color feature. Performance can be compared to an existing system by evaluating the time, retrieval precision and recall. The experiment is performed on Corel dataset and it shows superior performance than the previous system.

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“…For color feature extraction hsv histogram, Gray Level Co-occurrence Matrix (GLCM ) for texture feature extraction and mo ment invariant method for shape feature extract ion was used . Co mbination of these three features with grab cut algorithm strongly improves precision and recall value [2]. Concept learning technique with fuzzy k-NN classification and relevance feedback technique has proposed algorith m where color, texture and shape feature were extracted using different techniques.…”

Section: International Journal Of Advance Engineer Ing and Research Dmentioning

confidence: 99%

“…The f irst coordinate represents hue, the second saturation, and the third, value, thereby giv ing HSV. Quantizat ion in terms of color histograms refers to the process of reducing the number of bins by taking colors that are very similar to each other and putting them in the same bin [2]. Obviously quantization reduces the informat ion regarding the content of images but as was mentioned this is the tradeoff when one wants to reduce processing time [10].…”

Section: Proposed Approachmentioning

confidence: 99%

Feature Based Image Retrieval Using Classification and Relevance Feedback Technique

2015

IJAERD

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Content-Based Image Retrieval Research

Cited by 40 publications

References 5 publications

Two Tier Approach for Automatic Retrieval of MRI Brain Image by Feature Extraction

Two Tier Approach for Automatic Retrieval of MRI Brain Image by Feature Extraction

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

Feature Based Image Retrieval Using Classification and Relevance Feedback Technique

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