Coarse to fine K nearest neighbor classifier

Xu, Yong; Zhu, Qi; Fan, Zizhu; Qiu, Minna; Chen, Yan; Liu, Hong

doi:10.1016/j.patrec.2013.01.028

Cited by 124 publications

(52 citation statements)

References 24 publications

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“…In the meanwhile, SRC, CRC, LRC, coarse to fine K nearest neighbor classification (CFKNNC) [56], improvement to nearest neighbor classification (INNC), homotopy [7], primal augmented lagrangian method (PLAM) [60], the method [65], discriminative sparse representation method (DSRM) [59], block-diagonal representation (BDLRR) [66] and the method [27] were used to compare with our method. Moreover, we set the parameter μ of our method as 0.01.…”

Section: Resultsmentioning

confidence: 99%

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A novel grouped sparse representation for face recognition

Peng

Liu

et al. 2018

Multimed Tools Appl

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Grouped sparse representation classification methods (GSRCMs) have been attracted much attention by scholars, especially in face recognition. However, pervious literatures of GSRCMs only fuse the scores from different groups to classification the test sample, not consider relationships of the groups. Moreover, in real-world application, many methods of face recognition cannot obtain satisfied recognition accuracies because of the variation of poses, illuminations and facial representations of face image. In order to overcome above-mentioned bottlenecks, in this paper, we proposed a novel grouped fusion-based method in face recognition. The proposed method uses the axis-symmetrical property of face to designs a framework and perform it on original training set to generate a kind of virtual samples. The virtual samples are able to reflect the possible change of face images. Meanwhile, to consider the relationship of different groups and strengthen the representation capability of test sample, the proposed method exploits a novel weighted fusion approach to classify the test sample. Experimental results on five face databases demonstrate that our

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Section: Resultsmentioning

confidence: 99%

“…But in real-world applications, because of the complex of evaluation, densitybased score fusion is hard to use. Pervious proposed literatures [11,12,18,49] reflect that if the score fusion methods were integrated into RBCMs, especially the GSRCMs [48,56,62], the test sample is more possibly to be assigned to the correct class.…”

Section: Introductionmentioning

confidence: 99%

A novel grouped sparse representation for face recognition

Peng

Liu

et al. 2018

Multimed Tools Appl

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“…In our experiments, we used an evaluation protocol similar to nearest feature space DWT [17], (NFS) [19], CFKNNC [19] and TPTSR [20]. In this protocol, the palmprint images gathered in the first session were selected as the training set and all of the samples from the second session were employed as the testing set (six training sets and six test sets).…”

Section: Protocol Imentioning

confidence: 99%

“…8 LDA ROC curves for the PCLBP and PHOG descriptors under the blue, green, red and NIR spectra using six images captured in the first session for the training set and six images captured in the second session for the testing set Neural Comput & Applic [19]. For validation purposes, we followed the standard evaluation protocol as given in [18,19], using the first three images under the blue, red, green and NIR spectra for the training set and six palms from the second session for the testing set. Moreover, all parameters of the proposed methods were initialized as described in Protocol I.…”

Section: Protocol IImentioning

confidence: 99%

“…The assessment of the performance of our proposed approaches was conducted using the multispectral palmprint database available from the Hong Kong Polytechnic University (PolyU), using LDA classification. In addition, a comparative analysis of our proposed algorithms was performed against a number of stateof-the-art counterparts, including the techniques described in [17][18][19][20]. The main body of this paper is composed of six main sections.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

El-Tarhouni

Boubchir

Elbendak

et al. 2017

Neural Comput & Applic

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Local binary pattern (LBP) algorithm and its variants have been used extensively to analyse the local textural features of digital images with great success. Numerous extensions of LBP descriptors have been suggested, focusing on improving their robustness to noise and changes in image conditions. In our research, inspired by the concepts of LBP feature descriptors and a random sampling subspace, we propose an ensemble learning framework, using a variant of LBP constructed from Pascal's coefficients of n-order and referred to as a multiscale local binary pattern. To address the inherent overfitting problem of linear discriminant analysis, PCA was applied to the training samples. Random sampling was used to generate multiple feature subsets. In addition, in this work, we propose a new feature extraction technique that combines the pyramid histogram of oriented gradients and LBP, where the features are concatenated for use in the classification. Its performance in recognition was evaluated using the Hong Kong Polytechnic University database. Extensive experiments unmistakably show the superiority of the proposed approach compared to state-of-the-art techniques.

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An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Nasir

Khan

Sharif

et al. 2018

Microscopy Res & Technique

143

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Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.

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Coarse to fine K nearest neighbor classifier

Cited by 124 publications

References 24 publications

A novel grouped sparse representation for face recognition

A novel grouped sparse representation for face recognition

Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

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