Color texture description with novel local binary patterns for effective image retrieval

Singh, Chandan; Walia, Ekta; Kaur, Kanwal Preet

doi:10.1016/j.patcog.2017.10.021

Cited by 99 publications

(53 citation statements)

References 36 publications

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“…Since then it is widely used for surveillance and security. 54 LBP is also used for the color texture descriptors 55 and face recognition. 56 LBP is a statistical method that summarizes the local structure of the image efficiently by comparing each pixel with all surrounding neighboring pixels, 57 through LBP one can easily define the image texture by two complementary measures, that is, "local spatial patterns" and "grayscale contrast.…”

Section: Local Binary Patternmentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

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Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Local Binary Patternmentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

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“…e texture descriptors can be used to characterize image textures or regions, where the local binary pattern (LBP) is a famous texture descriptor [11]. In recent years, some LBP methods have been proposed for content-based image retrieval [12][13][14], such as the multichannel decoded LBP method [12] and the edge-local binary pattern (edgeLBP) approach [14]. In recent years, many algorithms have been proposed that combine multivisual cues [3-10, 12, 13] and have been utilized for various tasks.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval Based on a Multi-Integration Features Model

Chu

Liu

2020

Mathematical Problems in Engineering

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Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

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“…Meanwhile, the LBP feature was extracted from Hue channel for HSL images (LBPH), gray images (LBPC) and then combined or fused to the colour histogram. The aim is to enhance the accuracy of image retrieval [13].…”

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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Color texture description with novel local binary patterns for effective image retrieval

Cited by 99 publications

References 36 publications

Study of statistical methods for texture analysis and their modern evolutions

Study of statistical methods for texture analysis and their modern evolutions

Image Retrieval Based on a Multi-Integration Features Model

Integration Colour and Texture Features for Content-based Image Retrieval

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