Texture Classification using a Linear Configuration Model based Descriptor

Guo, Yuhang; Zhao, Guoying; Pietikäinen, Matti

doi:10.5244/c.25.119

Cited by 67 publications

(68 citation statements)

References 23 publications

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“…The LBP-based feature extractor has proven to be highly distinctive and its key advantages, including the robustness against illumination and pose variations, and the computational efficiency, make it suitable for high level image analysis tasks. Despite the great success of LBP in computer vision and pattern recognition, the various extensions and modifications of this texture extraction approach have been proposed [5]- [7]. The first approach on classification of gender of Drosophila using their wing's texture was proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

Fly Wing Biometrics Using Modified Local Binary Pattern, SVMs and Random Forest

Ahmad¹,

Roy²,

O'Connor³

et al. 2014

IJMLC

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Abstract-This paper presents an efficient approach for classification of the gender of a common fruit fly, Drosophila melanogaster, based on their wing's texture. The novelty of this research effort is that a Modified Local Binary Pattern (MLBP), which combines both the sign and magnitude features for the improvement of fly wing's texture classification performance, is applied. The extracted features are then used to classify the gender of the fruit fly by using the Support Vector Machines (SVMs) and Random Forest (RF). We validate the performance of the proposed scheme on two fly wing datasets. The highest accuracy achieved by the proposed approach is 94%. In this paper, we limit our approach to gender classification; however, this effort can be extended to explore important characteristics of a fly using wing's texture analysis.Index Terms-Biometrics, fly wing, modified local binary pattern, random forest, and support vector machine.

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Section: Introductionmentioning

confidence: 99%

Fly Wing Biometrics Using Modified Local Binary Pattern, SVMs and Random Forest

Ahmad¹,

Roy²,

O'Connor³

et al. 2014

IJMLC

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“…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

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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.

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“…• Features based on configuration information, such as Local Configuration Pattern (LCP) combines local structural [129] and microscopic [130] configuration information which can be used for image classification. That hybrid feature extraction method has potential.…”

Section: Feature Extraction and Assessmentmentioning

confidence: 99%