Pairwise Cross Pattern: A Color-LBP Descriptor for Content-Based Image Retrieval

Hao, Qiaohong; Feng, Qinghe; Wei, Ying; Sbert, Mateu; Lu, Wenhuan; Xu, Qing

doi:10.1007/978-3-030-00776-8_27

Cited by 4 publications

(9 citation statements)

References 19 publications

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“…In addition, the open source implementation of the LBP feature map [9,10] can be downloaded from http://www.ee.oulu.fi/~{}gyzhao/. Referring to [6,22,23,27,40,49,50], all images in the dataset are considered as the query images to guarantee the accurateness and reproducibility. Referring to [6,22,23,27,40,49,50], the number of returned images on VisTex, KTH-2a, Face-95 and Corel-1000 is defined as 10, and the number of returned images on ETHZ and ZuBuD is defined as five because there are only five images in each class.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, based on the sorting results of the similarity measure, the top similar images are returned to the user. Referring to [6,22,33,42], extended Canberra distance (ECD) is utilized in this paper, and the ECD is defined as follows:…”

Section: Similarity Measurementioning

confidence: 99%

“…Therefore, the problem of extracting effective, robust and practical features has attracted an increasing number of researchers. Thanks to these pioneers' breakthroughs, many approaches [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] have been continuously proposed and extended for the task of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

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Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Feng

Wei

et al. 2019

Applied Sciences

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With the advent of medical endoscopes, earth observation satellites and personal phones, content-based image retrieval (CBIR) has attracted considerable attention, triggered by its wide applications, e.g., medical image analytics, remote sensing, and person re-identification. However, constructing effective feature extraction is still recognized as a challenging problem. To tackle this problem, we first propose the five-level color quantizer (FLCQ) to acquire a color quantization map (CQM). Secondly, according to the anatomical structure of the human visual system, the color quantization map (CQM) is amalgamated with a local binary pattern (LBP) map to construct a local ternary cross structure pattern (LTCSP). Third, the LTCSP is further converted into the uniform local ternary cross structure pattern (LTCSPuni) and the rotation-invariant local ternary cross structure pattern (LTCSPri) in order to cut down the computational cost and improve the robustness, respectively. Finally, through quantitative and qualitative evaluations on face, objects, landmark, textural and natural scene datasets, the experimental results illustrate that the proposed descriptors are effective, robust and practical in terms of CBIR application. In addition, the computational complexity is further evaluated to produce an in-depth analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Similarity Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Feng

Wei

et al. 2019

Applied Sciences

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“…Since a huge number of image corpora have been produced by visual sensor equipment , an increasing demand for efficient encoding and indexing of these image corpora has attracted the attention of a considerable number of researchers [1,2,3,4,5,6,7]. Thanks to the investigators’ breakthroughs, a myriad of methods [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] have continuously been developed for encoding and indexing.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a series of color texture descriptors [17,18,19,20,21,22,23,24,25,26,27,28,29] have been sequentially developed for color image processing. Among them, the local oppugnant color texture pattern (LOCTP) [17], a variant of the multi component LTrP, was combined with the colored pattern appearance model (CPAM) in the YCbCr, HSV, and RGB color spaces.…”

Section: Introductionmentioning

confidence: 99%

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

Feng

Hao

Sbert

et al. 2019

Sensors

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Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.

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ChartFuse: a novel fusion method for chart classification using heterogeneous microstructures

Mishra

Kumar

Chaube

2020

Multimed Tools Appl

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Pairwise Cross Pattern: A Color-LBP Descriptor for Content-Based Image Retrieval

Cited by 4 publications

References 19 publications

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval

ChartFuse: a novel fusion method for chart classification using heterogeneous microstructures

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