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1 Handcrafted vs: non-handcrafted features for computer vision classification
ویژگی های هنر دستی در مقابل غیر دستی برای طبقه بندی بینایی ماشین-2017
Article history:Received 26 May 2016Revised 16 April 2017Accepted 28 May 2017Available online 3 June 2017Index Terms: Deep learning Transfer learningNon-handcrafted features Texture descriptors Texture classification Ensemble of descriptorsThis work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features. Such a system is a single structure that can be used for synthesizing a large num- ber of different image classification tasks. Three substructures are proposed for creating the generic com- puter vision system starting from handcrafted and non-handcrafter features: i) one that remaps the out- put layer of a trained CNN to classify a different problem using an SVM; ii) a second for exploiting the output of the penultimate layer of a trained CNN as a feature vector to feed an SVM; and iii) a third for merging the output of some deep layers, applying a dimensionality reduction method, and using these features as the input to an SVM. The application of feature transform techniques to reduce the dimen- sionality of feature sets coming from the deep layers represents one of the main contributions of this paper. Three approaches are used for the non-handcrafted features: deep transfer learning features based on convolutional neural networks (CNN), principal component analysis network (PCAN), and the com- pact binary descriptor (CBD). For the handcrafted features, a wide variety of state-of-the-art algorithms are considered: Local Ternary Patterns, Local Phase Quantization, Rotation Invariant Co-occurrence Local Binary Patterns, Completed Local Binary Patterns, Rotated local binary pattern image, Globally Rotation Invariant Multi-scale Co-occurrence Local Binary Pattern, and several others. The computer vision system based on the proposed approach was tested on many different datasets, demonstrating the generaliz- ability of the proposed approach thanks to the strong performance recorded. The Wilcoxon signed rank test is used to compare the different methods; moreover, the independence of the different methods is studied using the Q-statistic. To facilitate replication of our experiments, the MATLAB source code will be available at (https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0).© 2017 Elsevier Ltd. All rights reserved.
Keyword: Deep learning | Transfer learning | Non-handcrafted features | Texture descriptors | Texture classification | Ensemble of descriptors
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