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34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019

作者： Zhang, Ke Wang, Xinsheng Guo, Yurong Zhao, Zhenbing Ma, Zhanyu North China Electric Power University Department of Electronic and Communication Engineering Hebei China Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728137230

Cross-entropy loss function (CEL) is widely used for training a multi-class classification deep convolutional neural network (DCNN). While CEL has been successfully implemented in image classification tasks, it only focuses on the posterior probability of correct class when the labels of training images are one-hot. It cannot be discriminated against the classes not belong to correct class (wrong classes) directly. Negative Log Likelihood Ratio Loss (NLLR) is proposed to better discriminate the correct class from competing wrong classes. But optimization of the loss function is normally presented as a minimization problem. In training DCNN, the value of NLLR is not constantly positive or negative, which affects the convergence of NLLR adversely. So, we propose competing ratio loss (CRL), which calculates the posterior probability ratio between the correct class and competing wrong classes to better widen the difference between the probability of the correct class and the probabilities of wrong classes, which also assures the value of CRL is constantly positive. Through massive experiments, we demonstrate the effectiveness and robustness of CRL on deep convolutional neural networks, our CRL outperforms CEL and NLLR on CIFAR-10/100 datasets. © 2019 IEEE.

关键词： Image classification

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intelligent Parking Big Data Cloud Platform Algorithm Research and Application 6

Intelligent Parking Big Data Cloud Platform Algorithm Resear...

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6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Yan, Xiaobo Zou, Xue Cai, Shan Wang, Lin Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Guiyang China College of Date Science and Information Engineering Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510668546

With the rapid development of the domestic economy and the increasing living standards of the people, the ownership of private cars has increased explosively. Currently, urban traffic congestion and parking difficulty have gradually become a hot topic of research from all walks of life. Parking difficulty is mainly reflected on the following aspects: first, the existing parking spaces in cities are not effectively utilized;The second reason is that drivers cannot grasp relevant information about parking spaces near the ground in real time. This article conducts in-depth research on the issue of parking difficulty. Based on the existing security monitoring systems in parking lots, real-time detection of parking spaces status is conducted based on computer vision, machine learning, and other methods. By building a comprehensive, synthesize, and multi-dimensional parking resources dynamic sensing system, a smart parking big data cloud platform is constructed to achieve the effect of intelligent indoor parking lots management. © 2023 SPIE.

关键词： Machine learning

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Tempo Variation Based Multilayer Filters for Query by Humming

Tempo Variation Based Multilayer Filters for Query by Hummin...

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International Conference on pattern recognition

作者： Qiang Wang Zhiyuan Guo Baoxiang Li Gang Liu Jun Guo Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications

ISBN: (纸本)9781467322164

In this paper we propose a methodology of multilayer filters based on tempo variation for realizing a query by humming (QBH) system. Firstly the original query clip is used to search for the candidate songs. If the results are unreliable, the clip is linearly scaled twice for more candidates. If the results are still unreliable, the clip is scaled more times for retrieval. To sort all the candidates, a new matching algorithm called key transposition recursive alignment (KTRA) is presented, which improves the retrieval accuracy. Experimental results on the 2010 MIREX QBH query corpus show that the proposed method can achieve a relative improvement of 20.9% as well as an acceleration factor of 2.09 simultaneously compared to a state-of-the-art method.

关键词： music query processing

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Multi-feature content-based product image retrieval based on region of main object

Multi-feature content-based product image retrieval based on...

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8th International Conference on Information, Communications and Signal Processing, ICICS 2011

作者： Chai, Lunshao Zhang, Honggang Qin, Zhen Yu, Jie Qi, Yonggang Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Riverside Lab for Artificial Intelligence Research University of California Riverside Riverside CA United States

ISBN: (纸本)9781457700309

Content-based image retrieval (CBIR) has got an intense interest and seen considerable progress over the last decade. But most of the time it is only applied in laboratory. One important reason for this is the diversity of images. Different practical situations call for different taxonomy definitions of images, and lead to very different solutions. At present, and even in the foreseeable future, a general purpose CBIR system is not really possible. However, image search engines oriented at specific domains are feasible in technology and also have the actual demand. With the rapid development of electronic commerce, searching specific product by image has become one of the most attractive related research topics. In this paper, we propose a region-based method fit for the content-based retrieval of product images. The method focuses on two key issues: fast extraction of the main region, in which the product locates, as well as efficient shape and color features extraction. To show the validity of the proposed region-based method, compared experiments are carried out and illustrated on the PI 100 dataset. © 2011 IEEE.

关键词： Content based retrieval

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Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization

Dual-attention Guided Dropblock Module for Weakly Supervised...

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International Conference on pattern recognition

作者： Junhui Yin Siqing Zhang Dongliang Chang Zhanyu Ma Jun Guo Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications

Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.

关键词： Location awareness Training Deep learning Adaptation models Visualization pattern recognition Automobiles

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WAF BASED TOPIC DETECTION IN MICRO-BLOG

WAF BASED TOPIC DETECTION IN MICRO-BLOG

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence systems

作者： Xiaoning Li Guang Chen Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications

Micro-blog is a new information sharing tool which is more and more popular these days. In very short time there are a large number of messages posted and it is difficult for people to grasp main topics that most people are talking about. In this paper, we introduce a new text modeling method, which is called WAF, and propose a feature selection method for topic clustering based on it. In the experiments this method shows promising results, and the subsequent clustering based on it works effectively as well.

关键词： Micro-blog topic detection WAF Clustering

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A bottom-up kernel of pattern learning for relation extraction

A bottom-up kernel of pattern learning for relation extracti...

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International Symposium on Chinese Spoken Language Processing

作者： Chunyun Zhang Weiran Xu Sheng Gao Jun Guo Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications

Measuring the similarity of patterns is the key in pattern-based approaches in relation extraction. Most existing methods generally rely on inflexible pattern similarity measurements which often lead to low recall. In this work, a novel kernel-based model is proposed to address this problem. Depending on the pattern similarities produced by our bottom-up kernel, the most similar semantic shortest dependency patterns are selected to update seed patterns in each iteration of bootstrapping. To obtain insights of the reliability and applicability of our proposed method, we applied it to the task of English Slot Filling (ESF) in Knowledge Base Population (KBP) track at Text Analysis Conference (TAC). The experimental results validate our proposed method that importantly improves the recall which resulting in the improvement of F1 value. The effectiveness of the bottom-up kernel is also verified by further experimental results.

关键词： Kernel Semantics Computational linguistics pattern matching Natural language processing Feature extraction Computer architecture

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A music retrieval system based on spoken lyric queries

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International Journal of Advancements in Computing Technology 2012年第8期4卷 173-180页

作者： Guo, Zhiyuan Wang, Qiang Liu, Gang Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing 100876 China

This paper proposes a spoken lyric search system, which can help users to find the wanted music based on their input spoken lyric phrases. Using spoken lyric phrases to query a music retrieval system is convenient for users but challenging for developers. In our proposed system, the spoken query will be converted into text using large vocabulary continuous speech recognition (LVCSR) technique at first, and then a list of candidate lyrics and their corresponding songs will be presented by the text retrieval techniques. To guarantee the performance of the system, we propose a novel approach to improving the current technology. Specifically, a string matching method, which is based on phoneme confusion matrix, is applied to search the most similar lyrics in acoustic respect for the multiple sentence hypotheses of the input voice;and a two-level search strategy is adopted to shorten the time consuming. Experimental results show that the proposed system achieved top-1 accuracy as high as 93.98% when the search is performed within 2,000 songs, and 83.20%, a moderately satisfying accuracy, was obtained when the targeted number of songs increased to 10,000.

关键词： Character recognition

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Boosting-POOF: Boosting Part Based One vs One Feature for Facial Expression recognition in the Wild

Boosting-POOF: Boosting Part Based One vs One Feature for Fa...

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International Conference on Automatic Face and Gesture recognition

作者： Zhiwen Liu Shan Li Weihong Deng Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications

Recent years, facial expression recognition has remained a challenging and interesting problem, especially for faces in the real world. Most of the traditional approaches are based on Action Units (AUs) detection or low-level features (e.g. LBP, HOG, SIFT and Gabor). Thus, when recognizing real-world facial expressions, these methods might result in poor performance. In this paper, we propose an automatic framework called 'Boosting-POOF' to extract discriminative Mid-Level features using low-level features extracted from local face regions. Rather than cascade local features altogether, we adopt class-pairwise Mid-Level descriptors for each local region to extract Mid-Level features and Adaboost feature selection to choose more discriminative features. In experiments, four facial expression benchmarks (CK+, SFEW, RAF-BASIC, RAFCOMPOUND) are evaluated. The 'Boosting-POOF' achieves state-of-the-art performance compared with recent approaches. What' more, the 'Boosting-POOF' can automatically provide the most significant difference between two expression categories, which is more useful than AUs detection for real world images.

关键词： AUs expression categories interesting

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Image spam classification based on convolutional neural network

Image spam classification based on convolutional neural netw...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Er-Xin Shang Hong-Gang Zhang Pattern Recognition and Intelligent System Laboratory Beijing University of Post and Telecommunication

ISBN: (纸本)9781509003914

Image classification is a fundamental problem in computer vision and pattern recognition. Feature extraction is often regarded as the key for classifying images. Traditional ways rely on handcrafted features heavily, such as SIFT and BoW. In this paper, we concentrate on recognizing some specific categories of images (e.g. adult content and political images) in Email. And most importantly we propose a novel architecture of Convolutional Neural Network (CNN) to apply on image classification. In particular, most of the previous methods often use softmax activation for prediction, while instead of utilizing a softmax layer, we present a linear support vector machine (SVM) to be a part of the learning model, minimizing a margin-based loss to obtain a lower level feature representation. Accordingly, we propose a new dataset which contains 7 categories and over 52934 images totally. Experimental results on this novel dataset validate the effectiveness of our method.

关键词： Support vector machines Feature extraction Electronic mail Databases Training Fasteners Cybernetics

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