An Efficient Content Based Image Retrieval System Based on Color Space Approach Using Color Histogram and Color Correlogram

Soni, Devyani; Mathai, K. James

doi:10.1109/csnt.2015.80

Cited by 16 publications

(5 citation statements)

References 5 publications

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“…Dubey et al [38] suggested the quantization of RGB colorspace to a single channel for retrieving color and texture details and introduced fused features named as a scale-invariant hybrid image descriptor (RSHD). Soni et al [39] proposed a method combining color histogram and color correlogram in their paper to enhance performance. In that article, they compared the results with color histogram, and the proposed approach works fine with the Wang dataset.…”

Section: Traditional Retrieval Practicesmentioning

confidence: 99%

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Chavda

Goyani

2020

SN COMPUT. SCI.

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The objective of the content-based image retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependence of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors: global color histogram and multi-scale local binary pattern (MS-LBP); furthermore, the use of PCA for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets, viz., Corel-1k, Corel-5k, Corel-10k, and Ghim-10k together with result comparison based on the precision and recall values at different thresholds. The classification purposes are satisfied with Euclidean and City Block distance. The performance study of the proposed work displays it as outperformer than the identified state-of-the-art literature.

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Section: Traditional Retrieval Practicesmentioning

confidence: 99%

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Chavda

Goyani

2020

SN COMPUT. SCI.

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“…Many researchers have proposed work in this field some of the most remarkable works are shown in this section. Devyani Soni and K. J. Mathai [2] have presented a technique for image retrieval dependent upon text, color space methodologies by means of color correlogram and color histogram. Color space methods utilized global color histogram and local color histogram and those are putted on both color spaces-RGB color space and HSV color space, and then they were compared.…”

Section: Literature Surveymentioning

confidence: 99%

“…Many times just one keyword is redundantly used with more than one images, therefore it leads to erroneous outcomes. Consequently, Content Based Image Retrieval (CBIR) is evolved to defeat the restriction of text based retrieval [2]. There are two fundamental principles of Content Based Image Retrieval systems for the image retrieval and they are-feature extraction and matching.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Method for Content Based Image Retrieval

Chhabra¹,

Birchha²

2015

IJERT

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The content based image retrieval (CBIR) is the wellliked and heart favorite area of research in the field of digital image processing. The key goal of content based image retrieval (CBIR) is to excerpt the visual content of an image directly, like color, texture, or shape. There are several applications of the CBIR technique such as forensic laboratories, crime detection, image searching etc. For the purpose of feature extraction of well-matched images from the database, a universal CBIR system utilizes texture, color and shape based techniques. In this presented work, we have offered an efficient approach for the content based image retrieval, where images are decomposed using the wavelet transform, it means that the image features are converted in the matrix form and a color feature data set is prepared. In order to improve search results we have used k-means algorithm. It is shown by experimental results that, the efficiency of the proposed method is improved in contrast with the existing method.

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“…It is the most intuitive and obvious feature of an image and is robust to changes in noise, image size, orientation, and resolution (Alzu’Bi et al , 2015). Many color feature descriptors have been proposed, including the CH (Swain and Ballard, 1991; Zeng et al , 2014; Huang et al , 2016), the color moments (CMs) (Stricker and Orengo, 1995; Islam and Debnath, 2016; Dandotiya and Atre, 2017), the color coherence vector (CCV) (Pass and Zabih, 1996; Jiexian et al , 2014; Salmi and Boucheham, 2016), the color correlogram (CC) (Huang, 1999; Rasheed et al , 2008; and Soni and Mathai, 2015;) and the color sets (CSs) (Smith and Chang, 1996; Mlsna and Rodríguez, 2000). The CH, first proposed by Swain and Ballard (1991), is the most commonly used color feature description method, shows the distribution of color information in images with translation, rotation and scaling invariance.…”

Section: Introductionmentioning

confidence: 99%

Review of image low-level feature extraction methods for content-based image retrieval

Wang

Han

Jin

2019

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Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

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An Efficient Content Based Image Retrieval System Based on Color Space Approach Using Color Histogram and Color Correlogram

Cited by 16 publications

References 5 publications

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

An Enhanced Method for Content Based Image Retrieval

Review of image low-level feature extraction methods for content-based image retrieval

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