A New Approach of Content Based Image Retrieval Using Color and Texture Features

Hossain, Md. Sazzad; Islam, Saiful

doi:10.9734/bjast/2017/33326

Cited by 10 publications

(8 citation statements)

References 19 publications

(13 reference statements)

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“…Meanwhile, the filtering process is performed to reduce the noise and to increase the quality of the image (Singh and Hemachandran, 2012;Hossain and Islam, 2017;Mamat et al, 2015). To attain this aim, we apply the median filter to an images Median filter is used because this filtering is performing better than the average filtering in the sense of removing impulse noise (Manoharan and Sathappan, 2013;Kannan et al, 2010;Szeliski, 2010;Malviya et al, 2017).…”

Section: Step 1: Pre-processingmentioning

confidence: 99%

“…This is because it is easy to extract rather than shape and texture. In addition, the color feature is relatively robust to background complication and independent of image size and orientation and this has attracted many researchers to use it in their research (Mamat et al, 2016a;Singh and Hemachandran, 2012;Hossain and Islam, 2017;Mamat et al, 2015). Taking advantage of these, color features were used in this study.…”

Section: Step 3: Colour Features Extractionmentioning

confidence: 99%

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Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

al.¹

2022

Int. j. adv. appl. sci.

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Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).

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Section: Step 1: Pre-processingmentioning

confidence: 99%

Section: Step 3: Colour Features Extractionmentioning

confidence: 99%

Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

al.¹

2022

Int. j. adv. appl. sci.

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“…Different techniques for image retrieval have been developed and they are classified into two approaches: Text-based image retrieval (TBIR) and CBIR [5]. TBIR was first introduced in 1970 for searching and retrieving images from image databases [6]. In TBIR, the images are denoted by text, and then the text is used to retrieve or search the images.…”

Section: Introductionmentioning

confidence: 99%

“…The main objective of CBIR techniques is to improve the efficiency of the system by increasing the performance using the combination of features [6]. Image features can be classified into two types: Local features and global features.…”

Section: Introductionmentioning

confidence: 99%

An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

Salih

Abdulla²

2021

UHD J SCI TECH

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Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.

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“…Until now, various methods have been implemented on the CBIR system i.e. : GCE-SVM [9], ENN-SA [10], ENN-BR [11], CO-FSS [12], ASI-GTR [13], CTF [14], and others. However, at this time, the problems that are often encountered with the CBIR system are a semantic gap and computational complexity [15]- [19].…”

Section: Introductionmentioning

confidence: 99%

SVM Relevance Feedback in HSV Quantization for CBIR

Pardede¹

2018

JCP

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In this research have implemented SVM relevance feedback technique in HSV quantization for CBIR. The proposed technique has two stages. The first stage performs image retrieval process based on results of distance measurement. The distance measurement used is Jeffrey Divergence with threshold 0.15. The second stage is image retrieval process based on SVM RF prediction model. The SVM RF model is formed based on user-provided feedback images. The users' feedback images are labeled as positive and others are negative. The purpose of this study is to determine the best value of the constant C on the linear kernel and the best value of the constant (C, G) on the RBF kernel. The best value of the constants provided generates the best model of SVM RF in the HSV Quantization method so that improve the performance of the CBIR system. Performance measurements are evaluated based on precision, recall, F-measure, and accuracy values. Based on the results of experiments that conduct on Wang dataset obtained that (C, G) = (2 2.725 , 2 2.725 ) is the best value on the RBF kernel. While C = 2 5.275 is the best value on SVM RF using linear kernel. The best of SVM RF technique is SVM RF using RBF kernel of second feedback. The SVM RF using RBF kernel increases the average precision value by 3.02%, the average recall value increasing amount 171.48%, the average F-Measure value increasing amount 80.34%, while the average accuracy value increasing amount 1.99% compared with the baseline. The SVM RF using RBF kernel obtains the best value on both the average recall value and the average F-Measure value compared to the state-of-the-art. and 296-315), 3 Saturations, step (0-0.2, 0.2-0.7 and 0.7-1.0) and 3 Values, step (0-0.2, 0.2-0.7 and 0.7-1.0), and with the same principle done for other schema. So for HSV Quantization (19,4,5) will produce 19*4*5 = 380 the number of distinct colors, whereas HSV Quantization (8,3,3) will produce 8*3*3 = 72 the number of distinct colors, and other HSV Quantization schemes.

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A New Approach of Content Based Image Retrieval Using Color and Texture Features

Cited by 10 publications

References 19 publications

Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

SVM Relevance Feedback in HSV Quantization for CBIR

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