Combined feature descriptor for Content based Image Retrieval

Selvarajah, S.; Kodithuwakku, Sarath S.

doi:10.1109/iciinfs.2011.6038060

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…In this study, the wavelet decomposition of grayscale image regions into four sub-images (LL, HL, LH and HH) [3] called wavelet or DWT sub-bands. Every sub-band is the result of a transformation that has a quarter of the original size of aimage before changing.…”

Section: Discrete Wavelet Transformsmentioning

confidence: 99%

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Application of content based image retrieval in digital image search system

2021

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Multimedia data is growing rapidly in the current digital era, one of which is digital image data. The increasing need for a large number of digital image datasets makes the constraints faced eventually drain a lot of time and cause the process of image description to be inconsistent. Therefore, a method is needed in processing the data, especially in searching digital image data in large image dataset to find image data that are relevant to the query image. One of the proposed methods for searching information based on image content is content based image retrieval (CBIR). The main advantage of the CBIR method is automatic retrieval process, compared to traditional keyword. This research was conducted on a combination of the HSV color histogram methods and the discrete wavelet transform to extract color features and textures features, while the chi-square distance technique was used to compare the test images with images into a database. The results have showed that the digital image search system with color and texture features have a precision value of 37.5% - 100%, with an average precision value of 80.71%, while the percentage accuracy is 93.7% - 100% with an average accuracy is 98.03%.

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Section: Discrete Wavelet Transformsmentioning

confidence: 99%

“…The CBIR method is used to index digital image datasets based on image color and texture features. Some research studies on CBIR have been used based on leaf color features [2], color and texture features [3][4][5]. Using gradient vector flow snake (GVFS) method and the CBIR technique in implementing an application [6].…”

Section: Introductionmentioning

confidence: 99%

Application of content based image retrieval in digital image search system

2021

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“…CBIR allows extracting the correct images according to objective visual contents of the image [1]. The aim of the CBIR systems is to provide means to find images in large repositories using its contents as low level descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…It is very effective, computationally efficient and because of its low complexity, it is popular in indexing applications [1].…”

Section: Introductionmentioning

confidence: 99%

Content-based Image Retrieval (CBIR) using Hybrid Technique

IbrahimAbood¹,

Muhsin²,

Tawfiq³

2013

IJCA

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Image retrieval is used in searching for images from images database. In this paper, content -based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level cooccurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is concluded that, for the database images used in this work, the CBIR using hybrid technique is better for image retrieval because it has a higher match performance (100%) for each type of similarity measure so; it is the best one for image retrieval.

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