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On Lemon Defect Recognition with visual feature extraction and Transfers Learning

Journal of Data Analysis and Information Processing 2021年第4期9卷 233-248页

作者： Yizhi He Tiancheng Zhu Mingxuan Wang Hanqing Lu Hua Lookeng Honors College Changzhou University Changzhou China

Applying machine learning to lemon defect recognition can improve the efficiency of lemon quality detection. This paper proposes a deep learning-based classification method with visual feature extraction and transfer learning to recognize defect lemons (i.e., green and mold defects). First, the data enhancement and brightness compensation techniques are used for data prepossessing. The visual feature extraction is used to quantify the defects and determine the feature variables as the bandit basis for classification. Then we construct a convolutional neural network with an embedded Visual Geometry Group 16 based (VGG16-based) network using transfer learning. The proposed model is compared with many benchmark models such as K-nearest Neighbor (KNN) and Support Vector Machine (SVM). Results show that the proposed model achieves the highest accuracy (95.44%) in the testing data set. The research provides a new solution for lemon defect recognition.

关键词： Machine Learning visual feature extraction Convolutional Neural Networks Transfer Learning

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Video Concept Detection Based on Multiple features and Classifiers Fusion

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China Communications 2012年第8期9卷 105-121页

作者： Dong Yuan Zhang Jiwei Zhao Nan Chang Xiaofu Liu Wei Beijing University of Posts and Telecommunications Beijing 100876 P. R. China France Telecom Research & Development Center Beijing 100190 P. R. China

The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systelrtically investigated, and kernelbased learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systen on both classifier-level and kernel-level fusion that contribute to a more robust system Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision （MAP） score is rmch better than the benchmark perforrmnce, which proves that our concepts detection engine develops a generic model and perforrrs well on both object and scene type concepts.

关键词： concept detection visual feature extraction kemel-based learning classifier fusion

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Structured Computational Modeling of Human visual System for No-reference Image Quality Assessment

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International Journal of Automation and computing 2021年第2期18卷 204-218页

作者： Wen-Han Zhu Wei Sun Xiong-Kuo Min Guang-Tao Zhai Xiao-Kang Yang MoE Key Lab of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China

Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of *** human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience *** traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational *** bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of *** to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,*** natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three *** support vector regression(SVR)is employed to aggregate features to the final quality *** experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

关键词： Image quality assessment(IQA) no-reference(NR) structural computational modeling human visual system visual feature extraction

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visual INFORMATION ASSISTED MANDARIN LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION

VISUAL INFORMATION ASSISTED MANDARIN LARGE VOCABULARY CONTIN...

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2003 International Conference on Natural Language Processing and Knowledge Engineering

作者： Peng Liu Zuoying Wang Department of Electronic Engineering Tsinghua University

ISBN: (纸本)0780379020

＜正＞In this paper,we present a general system framework of Mandarin audio-visual large vocabulary continuous speech recognition（LVCSR）,which integrates visual information for better recognition performance and *** problems of audio-visual LVCSR are mainly addressed:lip tracking,visual feature extraction and audio-visual ***,the linear transform based lip tracking and low-level visual feature extraction methods are presented in comparison with the lip contour based feature ***,the audio-visual fusion strategy based on multi-stream hidden Markov model（MSHMM） is investigated and a novel approach is presented for training global or state-dependent stream weights using minimum classification error（MCE） *** is shown by experimental results that,with the visual information introduced,the word error rate（WER） of LVCSR system is reduced by 36.09%relatively in the case of clean audio,and the system robustness is also enhanced significantly in noise environment.

关键词： Audio-visual speech recognition visual feature extraction Audio-visual fusion

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