PLBP: An effective local binary patterns texture descriptor with pyramid representation

Qian, Xueming; Hua, Xian-Sheng; Chen, Ping; Ke, Liangjun

doi:10.1016/j.patcog.2011.03.029

Cited by 188 publications

(93 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The method uses a combination of both global features extracted from the whole image and local SIFT features densely extracted from the image and then combined using a bag of feature approach. As global descriptor the authors adopts both textural features (the Local Configuration Pattern (LCP) by Yimo Guo and Pietikinen (2011), the rotation invariant co-occurrence among adjacent LBP (RIC-LBP) by Nosaka and Fukui (2014), the extended LBP (ELBP) by Liu et al (2012), the multiscale Pyramid LBP (PLBP) by Qian et al (2011)) and the Strandmark morphological features (STR) by Strandmark et al (2012). For each image a set of eight images is obtained by resizing and rotating the original one.…”

Section: Methods Facing Taskmentioning

confidence: 99%

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

Hobson

Lovell

Percannella

et al. 2016

Pattern Recognition Letters

View full text Add to dashboard Cite

1 Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• Updates the state of the art in HEp-2 cell and specimen image classification ABSTRACTThe Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial type 2 (HEp-2) cells is the current gold standard for the Antinuclear Antibody (ANA) test. The formulation of the diagnosis requires the visual analysis of a patient's specimen under a fluorescence microscope in order to recognize the cells' staining pattern which could be related to a connective tissue disease. This analysis is time consuming and error prone, thus in the recent past we have witnessed a growing interest in the pattern recognition scientific community directed at the development of methods for supporting this complex task. The main driver of the interest towards this problem is represented by the series of international benchmarking initiatives organized in the last four years that allowed dozens of research groups to propose innovative methodologies for HEp-2 cells' staining pattern classification. In this paper we update the state of the art on HEp-2 cells and specimens classification, by analyzing the performance achieved by the methods participating the contest on Performance Evaluation of IIF Image Analysis Systems, hosted by the 22nd edition of the International Conference on Pattern Recognition ICPR 2014, and to the Executable Thematic Special Issue of Pattern Recognition Letters on Pattern Recognition Techniques for IIF Images Analysis, and by highlighting the trends in the design of the best performing methods.

show abstract

Section: Methods Facing Taskmentioning

confidence: 99%

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

Hobson

Lovell

Percannella

et al. 2016

Pattern Recognition Letters

View full text Add to dashboard Cite

show abstract

“…It also combined with block-based method for image retrieval [16]. Many works were done to extend its invariant against rotation [17] and scale [18].…”

Section: Local Binary Patternmentioning

confidence: 99%

“…LBP was extended its feature against texture resolution by cascading the hierarchical spatial pyramids information of LBP [18]. Detail of this process was showed in Figure 7.…”

Section: Pyramid Of Lbpmentioning

confidence: 99%

Face Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study

Khoi¹,

Thien²,

Viet³

2016

ijacsa

View full text Add to dashboard Cite

faces and Caltech Faces 1999 (CF). Good result on these dataset has encouraged us to conduct tests on Labeled Faces in the Wild (LFW), where the images were taken from real-world condition. Mean average precision (MAP) was used for measuring the performance of the system. We carry out the experiments in two main stages indexing and searching with the use of k-fold cross-validation. We further boost the system by using Locality Sensitive Hashing (LSH). Furthermore, we also evaluate the impact of LSH on the searching stage. The experimental results have shown that LSH is effective for face searching as well as LBP is robust feature in fontal face retrieval.

show abstract

“…Variance in LBP (LBPV) [13] is utilized to encode local contrast information without requiring a quantization process, and rotation invariance is implemented by estimating principal orientations and aligning LBP histograms. By constructing a cascading spatial pyramid of LBP, Qian et al [37] introduced pyramid transformed LBP (PLBP), the robustness of PLBP was compared with those of other LBP variants in this works. Further, Liu et al suggested extended LBP [26] by a combination of pixel intensities and local differences.…”

Section: Introductionmentioning

confidence: 99%

A Handcrafted Normalized-Convolution Network for Texture Classification

Nguyen

Gosselin

2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

In this paper, we propose a Handcrafted NormalizedConvolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a predefined filter bank (Gabor filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.

show abstract

PLBP: An effective local binary patterns texture descriptor with pyramid representation

Cited by 188 publications

References 25 publications

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results

Face Retrieval Based On Local Binary Pattern and Its Variants: A Comprehensive Study

A Handcrafted Normalized-Convolution Network for Texture Classification

Contact Info

Product

Resources

About