Extraction of image features for an effective CBIR system

Reddy, P. Gangadhara

doi:10.1109/rstscc.2010.5712832

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Different combiner functions (e.g., average, operation, limit, and majority) are essential in this regard. The voting system only wants, given an object, a specific classifier for which it performs better for the output class [21]. We concentrate on our paper on ArgMax voting in particular.…”

Section: Classificationmentioning

confidence: 99%

Design and Implementation of Content-Based Image Retrieval on FPGA card

2020

IJATCSE

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In recent years, real-time Image Processing (IP) has seen some applications and has posed a fast-growing field of programmable gate arrays (FPGA) implementation that has been the essential subject of the current engineering trend their simplicity and immense potential. One of the main problems of some IP is image extraction, which is that you can retrieve valuable knowledge about the quality of the images from the data effectively. We define the implementation of CBIR in FPGA image processing applications. The fundamental goal behind this initiative is to meet the shortest timeline by the optimum use of services. We are undertaking a review of this embedded approach's benefits and discussing its usefulness with a series of benchmark images. Implementation based on the FPGA indicates the viability of the suggested technique. The practical implementation of all processes using the FPGA Verilog code has been compiled into the ALTERA QUARTUS-II software method. The results obtained show qualitatively and quantitatively the feasibility of the proposed procedure.

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Section: Classificationmentioning

confidence: 99%

Design and Implementation of Content-Based Image Retrieval on FPGA card

2020

IJATCSE

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“…By varying the separation vector it allows to capture different texture characteristics. After making the GLCM symmetrical, there is still one step to take before texture measures can be calculated [11].…”

Section: Use Of Glcm Matrix Computationmentioning

confidence: 99%

Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques

Lumb¹,

Sethi²

2013

IJCA

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When changing the format of an image from simple RGB to HSV, YIQ and Dithered image, the characteristics of image also change. In this paper, the similar images in the above formats are retrieved using statistical and structural retrieving techniques i.e. GLCM (Gray Level Co-occurrence Matrix) and Wavelet Decomposition techniques. The best results are coming for dithered, HSV images by using GLCM technique for feature extraction and by using Wavelet decomposition; HSV images are giving the best results. While other formats also give the correct retrieval but from the accuracy point of view, HSV images are retrieved with better accuracy using both techniques.

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“…Texture based feature extraction techniques such as co-occurrence matrix, Fractals, Gabor Filters, variations of wavelet transform, other transform have also been widely used [2], [3]. In Gray level Co-occurrence matrix (GLCM), the texture features from gray scale image are extracted [4]. As per GLCM, the following texture features are considered for identifying differentiating textures of images properties 1.1 Entropy: Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image.…”

Section: Introductionmentioning

confidence: 99%

Domain Specific CBIR for Highly Textured Images

sandhu¹

2013

CSEIJ

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Extraction of image features for an effective CBIR system

Cited by 7 publications

References 5 publications

Design and Implementation of Content-Based Image Retrieval on FPGA card

Design and Implementation of Content-Based Image Retrieval on FPGA card

Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques

Domain Specific CBIR for Highly Textured Images

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