Improving Local Binary Patterns Techniques by Using Edge Information

Abdesselam, Abdelhamid

doi:10.7763/lnse.2013.v1.77

Cited by 17 publications

(7 citation statements)

References 20 publications

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“…In [ 25 ], the Difference of Gaussians ( DoG ) filter was used to compute from a given image two maps representing the “positive” and the “negative” sides of the image edges, resulting in a classification accuracy improvement. A similar approach was exploited by [ 26 ] (details in section 2.3) for the extraction, through Sobel filtering, of an edge and a non-edge region from a texture image to compute LBP , LTP , etc. on the original image masked by each map.…”

Section: Introductionmentioning

confidence: 99%

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

et al. 2016

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AimsA fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification.MethodsFor image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level.Dataset and ResultsThe RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%.ConclusionsHere we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f.

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Section: Introductionmentioning

confidence: 99%

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

et al. 2016

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“…The results showed a significant improvement in the retrieval performance of the modified techniques. Preliminary results on LBP variants have been already published in [31]. In this work, two additional techniques (FFT, and DWT) are considered and one more dataset (Vistex) is included in the experiments.…”

Section: Introductionmentioning

confidence: 99%

Edge Information for Boosting Discriminating Power of Texture Retrieval Techniques

Abdesselam¹

2016

IJIGSP

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Abstract-Texture is a powerful image property for object and scene characterization, consequently, a large number of techniques has been developed for describing, classifying and retrieving texture images. On the other hand, edge information is proven to be an important cue used by the human visual system. Several physiological experiments have shown that, when looking at an object, human eyes explore different locations of that object through saccadic eye movements but they spend more time fixating edge regions. Based on this result, we hypothesize that a better performance could be obtained when analyzing an image (texture images in this case) if the visual features extracted from edge regions are given higher weights than those extracted from uniform regions. To check the validity of this hypothesis, we have modified several existing texture retrieval techniques in a way that incorporates the proposed idea and compared their performance with that of the original techniques. The results of the experiments that have been conducted on three common datasets confirmed the effectiveness of the proposed approach, since a significant improvement in the retrieval rate is obtained for all tested techniques. The experiments have also shown an improvement in the robustness to noise.

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“…Jabid et al [23] proposed a robust facial image descriptor to analyze toner and skin information effectively for recognizing face expression called Local Transitional Pattern. Abdesselam [24] proposed a method for improving LBP-based methods by constructing two different LBP histograms one for edge pixels and the second for non-edge pixels. However, the idea in [24] relies on salient regions, where human attention focuses and human observation is not verified.…”

Section: Related Workmentioning

confidence: 99%

“…Abdesselam [24] proposed a method for improving LBP-based methods by constructing two different LBP histograms one for edge pixels and the second for non-edge pixels. However, the idea in [24] relies on salient regions, where human attention focuses and human observation is not verified. An automated way for recognizing facial expression using gradient-based ternary texture patterns are presented in [25].…”

Section: Related Workmentioning

confidence: 99%

Similarity Estimation for Large-Scale Human Action Video Data on Spark

Uddin

Dolgorsuren

et al. 2018

Applied Sciences

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Abstract:The amount of human action video data is increasing rapidly due to the growth of multimedia data, which increases the problem of how to process the large number of human action videos efficiently. Therefore, we devise a novel approach for human action similarity estimation in the distributed environment. The efficiency of human action similarity estimation depends on feature descriptors. Existing feature descriptors such as Local Binary Pattern and Local Ternary Pattern can only extract texture information but cannot obtain the object shape information. To resolve this, we introduce a new feature descriptor, namely Edge based Local Pattern descriptor (ELP). ELP can extract object shape information besides texture information and ELP can also deal with intensity fluctuations. Moreover, we explore Apache Spark to perform feature extraction in the distributed environment. Finally, we present an empirical scalability evaluation of the task of extracting features from video datasets.

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Improving Local Binary Patterns Techniques by Using Edge Information

Cited by 17 publications

References 20 publications

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

Edge Information for Boosting Discriminating Power of Texture Retrieval Techniques

Similarity Estimation for Large-Scale Human Action Video Data on Spark

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