Wavelet Based Content Based Image Retrieval Using Color and Texture Feature Extraction by Gray Level Coocurence Matrix and Color Coocurence Matrix

Prabhu, Jeyanthi; Kumar, V. Jawahar Senthil

doi:10.3844/jcssp.2014.15.22

Cited by 17 publications

(9 citation statements)

References 4 publications

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“…For Wang database, we compared our approach with the three approaches presented in [19,20] and [21] and the results reported are very promising and provide better performance (see Fig. 15).…”

Section: Resultsmentioning

confidence: 99%

“…Other approaches were proposed for features selection and dimension reduction by using principal component analysis (PCA),wavelets, Ripplets and its derivations [14][15][16][17][18]. Others combined the visual content in order to increase the robustness and efficiency [19][20][21][22][23]. Also, Lande et al [24] presented an effective approach which combine color, texture and shape features based on the extraction of dominant color of each block, gray-level co-occurrence matrix (GLCM) and Fourier descriptors, respectively.…”

Section: Low-level Content Approachesmentioning

confidence: 99%

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A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

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Content-based image retrieval (CBIR) has been a worthwhile topic for research for several years. A powerful CBIR system should minimize the semantic gap between the low-level features and high-level concepts in order to satisfy users requirements. Moreover, it should take into consideration the execution time. In this paper, we present a new semantic approach for CBIR supported by a parallel aggregation of content-based features extraction (shape, texture, color) using fuzzy support decision mechanisms. Shape features are based on Fast Beta Wavelet Network modeling and Hue moments. The texture descriptor is based on Energy computing at different decomposition levels. Finally, we present an implementation of a new color feature extraction based on fuzzy indexed color map. In the second stage, we propose a Fuzzy Decision Support System for feature (shape, texture, color) aggregation to improve the retrieval performance. The proposed approach is tested on four most popular datasets: Wang, INRIA Holidays, UKBench and samples from ImageNet, and the experiments showed that B Asma ElAdel the proposed approach can achieve a satisfactory retrieval performance with an acceptable search time.

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Section: Resultsmentioning

confidence: 99%

Section: Low-level Content Approachesmentioning

confidence: 99%

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

ElAdel

Ejbali

Zaied

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…However, the use of histogram representations of features presents two primary drawbacks: the loss of spatial distribution information and the loss of information due to quantization. To address this, histograms can be augmented by the inclusion of additional spatial information and other local properties (Birchfield and Rangarajan, 2005; Lyons, 2009; Prabhu and Kumar, 2014; Zeng et al, 2015). In previous work, our group developed a method to simultaneously assess similarities in feature values and their regional distribution based on spatial histograms (Rolfe et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Associations Between Genetic Data and Quantitative Assessment of Normal Facial Asymmetry

2018

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Human facial asymmetry is due to a complex interaction of genetic and environmental factors. To identify genetic influences on facial asymmetry, we developed a method for automated scoring that summarizes local morphology features and their spatial distribution. A genome-wide association study using asymmetry scores from two local symmetry features was conducted and significant genetic associations were identified for one asymmetry feature, including genes thought to play a role in craniofacial disorders and development: NFATC1, SOX5, NBAS, and TCF7L1. These results provide evidence that normal variation in facial asymmetry may be impacted by common genetic variants and further motivate the development of automated summaries of complex phenotypes.

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“…The texture bears important visual information to represent and index images in database. Different approaches have been developed using GLCM [4,5,6,7] with other features such as Local Binary Patterns and Discrete Wavelet Transform to increase the effectiveness of representing images. In other words, each feature can capture different visual information that are exploited together to reduce the semantic gap issue as will be explained in Sec.…”

Section: Gray Level Co-occurance Matrix (Glcm)mentioning

confidence: 99%

“…Different colour, texture, colour texture, and shape features have been explored and used to represent images effectively in CBIR. Prabhu and Kumar [4] used colour histogram, texture GLCM, and texture GLCM from Discrete Wavelet Transform (DWT) features to represent images in CBIR. Three databases of extracted features were built using WANG standard database images.…”

Section: Related Workmentioning

confidence: 99%

Integration Colour and Texture Features for Content-based Image Retrieval

Al-Jubouri¹

2020

IJMECS

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Content-Based Image Retrieval offers an automatic way to extract visual image contents such as colour, texture, and shape so-called extracted features. Due to growing volume of digital images, Content-Based Image Retrieval is emerged to store and retrieved images from large scale databases. However, Content-Based Image Retrieval faces a challenge of meaning "Semantic gap" between machine and human conceptual. How to reduce this gap between colour and/or texture features that represent an object in the image? It is still the challenge that basically related to the effectiveness of image representation by extracted features and similarity measures between a query image features and database image features. Hence, different visual features have been proposed such as Gary Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT) texture features that are extracted from gray-scale images. This paper presents an unsupervised algorithm that exploits data and score-level fusion to address the semantic gap. The algorithm first extracts mentioned features from colour images in HSV and YCbCr colour spaces to increase the effectiveness of image representation by integrating texture and colour visual information in terms of data-level fusion. Resulted similarity retrieval values are then fused in three versions of score-level fusion, summing values without weights, fixed, and adaptive weights using linear regression to raise relevant images in a ranked retrieved images list. WANG standard colour images are used to implement the algorithm. Rates of achievement in image retrievals are enhanced at both levels.

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Wavelet Based Content Based Image Retrieval Using Color and Texture Feature Extraction by Gray Level Coocurence Matrix and Color Coocurence Matrix

Cited by 17 publications

References 4 publications

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

A hybrid approach for Content-Based Image Retrieval based on Fast Beta Wavelet network and fuzzy decision support system

Associations Between Genetic Data and Quantitative Assessment of Normal Facial Asymmetry

Integration Colour and Texture Features for Content-based Image Retrieval

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