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نتیجه جستجو - قابلیت استخراج

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Big data analytics enabled by feature extraction based on partial independence
تحلیل داده های بزرگ فعال شده توسط قابلیت استخراج بر اساس استقلال جزئی -2018
Complex cells in primary visual cortex (V1) selectively respond to bars and edges at a particular location and orientation. Namely, they are relatively invariant to the phase as well as selective to the frequency and orientation emerging from natural images that are analogous to the characteristics of complex cells in V1 with the energy function of receptive fields (RFs) from tuning curve test with sinusoidal function in our related jobs. In this paper, we propose a feature learning algorithm based on the overcomplete AISA to apply on big data in parallel computing. In order to demonstrate the effectiveness of the overcomplete AISA features in the classification task, two feature representation architectures are evolved into the par tial independent signal bases and partial independent factorial representation, respectively. Experiments on four datasets (Coil20, Extended YaleB, USPS, PIE), acquired conjunction with two classification archi tectures based on the overcomplete AISA features, show that the classification accuracy is mostly higher than those obtained from the other ICA related features and two other sparse representation features with a small number of training samples via nearest neighbor (NN) classification method.
Keywords: Independent Component(IC) ، Overcomplete features ، Sparse representation ، Big data
مقاله انگلیسی
2 Angular descriptors of complex networks: A novel approach for boundary shape analysis
توصیفگرهای زاویه ای شبکه های پیچیده: روش جدید برای تحلیل شکل مرزی-2017
Article history:Received 11 August 2016Revised 2 August 2017Accepted 3 August 2017Available online 3 August 2017Keywords:Shape analysis Complex networks Computer vision Feature extraction ClassificationWe introduce a method for shape recognition based on the angular analysis of Complex Networks. Our method models shapes as Complex Networks defining a more descriptive representation of the inner an- gularity of the shape’s perimeter. The result is a set of measures that better describe shapes if compared to previous approaches that use only the vertices’ degree. We extract the angle between the Complex Network edges, and then we analyze their distribution along with a network dynamic evolution. The proposed approach, named Angular Descriptors of Complex Networks (ADCN), presents a high discrimi- natory power, as evidenced by experiments conducted in five datasets. It is rotation invariant, presents high robustness against scale changes and degradation levels, overcoming traditional methods such as Zernike moments, Multiscale Fractal dimension, Fourier, Curvature and the degree-based descriptors of Complex Networks.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Shape analysis | Complex networks | Computer vision | Feature extraction | Classification
مقاله انگلیسی
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