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A music retrieval system based on spoken lyric queries

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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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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Instance-level coupled subspace learning for fine-grained sketch-based image retrieval 14

Instance-level coupled subspace learning for fine-grained sk...

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Computer Vision - ECCV 2016 Workshops, Proceedings

作者： Xu, Peng Yin, Qiyue Qi, Yonggang Song, Yi-Zhe Ma, Zhanyu Wang, Liang Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9783319466033

Fine-grained sketch-based image retrieval (FG-SBIR) is a newly emerged topic in computer vision. The problem is challenging because in addition to bridging the sketch-photo domain gap, it also asks for instance-level discrimination within object categories. Most prior approaches focused on feature engineering and fine-grained ranking, yet neglected an important and central problem: how to establish a finegrained cross-domain feature space to conduct retrieval. In this paper, for the first time we formulate a cross-domain framework specifically designed for the task of FG-SBIR that simultaneously conducts instancelevel retrieval and attribute prediction. Different to conventional phototext cross-domain frameworks that performs transfer on category-level data, our joint multi-view space uniquely learns from the instance-level pair-wise annotations of sketch and photo. More specifically, we propose a joint view selection and attribute subspace learning algorithm to learn domain projection matrices for photo and sketch, respectively. It follows that visual attributes can be extracted from such matrices through projection to build a coupled semantic space to conduct retrieval. Experimental results on two recently released fine-grained photo-sketch datasets show that the proposed method is able to perform at a level close to those of deep models, while removing the need for extensive manual annotations. © Springer International Publishing Switzerland 2016.

关键词： Learning algorithms

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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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Audio Fingerprinting Based on N-grams

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第10期6卷 361-368页

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

In this paper, we present a novel audio fingerprinting method based on N-grams, which can quickly identify a segment of audio even when the audio signals are seriously distorted. We make use of N peaks in spectrum to form the audio fingerprint, which accelerates the retrieval speed greatly. We take advantage of the initial robust peaks to calculate the similarity between candidates and the input audio, which improves the retrieval accuracy significantly. The effectiveness of the N-gram method was evaluated on a music database of 10,000 songs. Experimental results show that the proposed approach outperforms two state-of-the-art algorithms (Shazam and Philips Robust Hash) in both effectiveness (in terms of retrieval accuracy) and efficiency (in terms of average retrieval time).

关键词： Music

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Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

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Journal of Beijing Institute of Technology 2023年第5期32卷 562-578页

作者： Yufan Liu Kongming Liang Yinuo Jing Shen Wang Zhanyu Ma Yiming Li Yizhou Yu Yizhou Wang Jun Guo Pattern Recognition and Intelligent System Laboratory School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China. Center on Frontiers of Computing Studies School of Computer SciencePeking UniversityBeijing 100871China AI Lab Deepwise HealthcareBeijing 100089China

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain ***,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural *** overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline *** proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context *** exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical *** maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby *** experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

关键词： brain midline delineation refinement network structure prior connectivity regular loss

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Natural image matting based on image inpainting 2

Natural image matting based on image inpainting

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2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Zhang, Yuan Tan, Mian Zhou, Zhulian Yang, Yuan Liang, Yihui Feng, Fujian Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China University of Electronic Science and Technology China School of Computer Science Zhongshan528400 China

ISBN: (纸本)9781665492508

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions. © 2022 IEEE.

关键词： Deep neural networks

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HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character recognition

HCL2000 - A Large-scale Handwritten Chinese Character Databa...

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International Conference on Document Analysis and recognition

作者： Honggang Zhang Jun Guo Guang Chen Chunguang Li Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications China

In this paper, we present a large scale offline handwritten Chinese character database-HCL2000 which will be made public available for the research community. The database contains 3,755 frequently used simplified Chinese-characters written by 1,000 different subjects. The writerspsila information is incorporated in the database to facilitate testing on grouping writers with different background such as age, occupation, gender, and education etc. We investigate some characteristics of writing styles from different groups of writers. We evaluate HCL2000 database using three different algorithms as a baseline. We decide to publish the database along with this paper and make it free for a research purpose.

关键词： Large-scale systems Character recognition Image databases Handwriting recognition Performance evaluation Face recognition pattern recognition Laboratories Testing Object recognition

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