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arXiv 2017年

作者： Xu, Peng Yin, Qiyue Huang, Yongye Song, Yi-Zhe Ma, Zhanyu Wang, Liang Xiang, Tao Bastiaan Kleijn, W. 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 Communications and Signal Processing Group Victoria University of Wellington New Zealand

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

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Image-based characterization of the pulp flows

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pattern recognition and Image Analysis 2016年第3期26卷 630-637页

作者： Sorokin, M. Strokina, N. Eerola, T. Lensu, L. Karttunen, K. Kalviainen, H. Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Computer Vision Group Department of Signal Processing Tampere University of Technology PO Box 527 TampereFI-33101 Finland Cemis-Oulu Unit of Measurement Technology Kajaani University Consortium University of Oulu PO Box 127 KajaaniFI-87400 Finland School of Information Technology Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor Darul Ehsan46150 Malaysia

Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions. © 2016, Pleiades Publishing, Ltd.

关键词： pattern matching

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Head rotation classification using dense motion estimation and particle filter tracking

Head rotation classification using dense motion estimation a...

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International Conference on Electrical and Electronics Engineering, ELECO

作者： Filiz Gürkan Bilge Günsel Deniz Kumlu Multimedia Signal Processing and Pattern Recognition Group Istanbul Technical University Turkey

We propose a method that performs dense motion classification integrated with particle filter tracking for monitoring whether the viewer is involved in the screened content or not. We first perform the color based particle filtering that enables us tracking head of the user through the video sequence. It is followed by optical flow estimation via SIFT flow applied on the tracked regions. Finally the features extracted based on the viewer head rotation and location are fed into the random forest classifier to report the involvement level of the tracked person. It is shown that the used probabilistic motion estimation model with the support of tracking significantly reduces the computational complexity while it provides comparable performance with the state-of-the-art methods. The proposed scheme allows online monitoring the viewer therefore can be integrated to the interactive multimedia systems.

关键词： Head Image motion analysis Computer vision Tracking Particle filters Magnetic heads Motion estimation

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Comparison of appearance-based and geometry-based bubble detectors

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8671卷 610-617页

作者： Strokina, Nataliya Juránek, Roman Eerola, Tuomas Lensu, Lasse Zemčik, Pavel Kälviäinen, Heikki Tampere University of Technology Department of Signal Processing P.O. Box 527 Tampere33101 Finland Brno University of Technology Department of Computer Graphics and Multimedia Brno Czech Republic Lappeenranta University of Technology Machine Vision and Pattern Recognition Laboratory P.O. Box 20 Lappeenranta53851 Finland

Bubble detection is a complicated tasks since varying lighting conditions changes considerably the appearance of bubbles in liquid. The two common techniques to detect circular objects such as bubbles, the geometry-based and appearance-based approaches, have their advantages and weaknesses. The geometry-based methods often fail to detect small blob-like bubbles that do not match the used geometrical model, and appearance-based approaches are vulnerable to appearance changes caused by, e.g., illumination. In this paper, we compare a geometry-based concentric circular arrangements (CCA) and appearance-based sliding window methods as well as their combinations in terms of bubble detection, gas volume computation, and size distribution estimation. The best bubble detection performance was achieved with the sliding window method whereas the most precise volume estimate was produced by the CCA method. The combination of the two approaches gave only a minor advantage compared to the base methods. © Springer International Publishing Switzerland 2014.

关键词： Geometry

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When Face recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face recognition

When Face Recognition Meets with Deep Learning: An Evaluatio...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Guosheng Hu Yongxin Yang Dong Yi Josef Kittler William Christmas Stan Z. Li Timothy Hospedales Centre for Vision Speech and Signal Processing University of Surrey UK Indicates equal contribution LEAR team Inria Grenoble Rhone-Alpes Montbonnot France Electronic Engineering and Computer Science Queen Mary University of London UK Chinese Academy of Sciences Center for Biometrics and Security Research & National Laboratory of Pattern Recognition China

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

关键词： Face recognition Face Databases Measurement Training Object recognition Convolutional codes

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Density-Aware Part-Based Object Detection with Positive Examples

Density-Aware Part-Based Object Detection with Positive Exam...

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International Conference on pattern recognition

作者： Ekaterina Riabchenko Joni-Kristian Kämäräinen Ke Chen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Deparment of Signal Processing Tampere University of Technology

ISBN: (纸本)9781479952106

Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object parts (visual codebook) and discriminative learning using positive and negative class examples. Distinction of our method from the existing part-based methods for object detection is the use of sparse class-specific landmarks with semantic meaning. The landmarks are the additional distinguished information of object location in the proposed framework. Additionally, localising semantic and discriminative landmarks (object parts) is significant in other related applications of computer vision, such as facial expression recognition and pose/orientation estimation of objects. Therefore, we propose a model which deviates from the mainstream by the fact that the object parts' appearance and spatial variation, constellation, are explicitly modelled in a generative probabilistic manner. With using only positive examples our method can achieve object detection accuracy comparable to state-of-the-art discriminative method.

关键词： Training Probabilistic logic Estimation Face Feature extraction Object detection Detectors

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Learning Generative Models of Object Parts from a Few Positive Examples

Learning Generative Models of Object Parts from a Few Positi...

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International Conference on pattern recognition

作者： Ekaterina Riabchenko Joni-Kristian Kämäräinen Ke Chen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Department of Signal Processing Tampere University of Technology

ISBN: (纸本)9781479952106

A number of computer vision problems such as object detection, pose estimation, and face recognition utilise local parts to represent objects, which include the distinguished information of objects. In this work, we introduce a novel probabilistic framework which automatically learns class-specific object parts (landmarks) in generative-learning manner. Encouraged by the success in learning and detecting facial landmarks, we employ bio-inspired multi-resolution Gabor features in the proposed framework. Specifically, complex-valued Gabor filter responses are first transformed to landmark specific likelihoods using Gaussian Mixture Models (GMM), and then efficient response matrix shift operations provide detection over orientations and scales. We avoid the undesirable characteristic of generative learning, a large number of training instances, with the novel concept of randomised Gaussian mixture model. Extensive experiments with public benchmarking Caltech-101 and BioID datasets demonstrate the effectiveness of our proposed method for localising object landmarks.

关键词： Feature extraction Gaussian mixture model Face Training Motorcycles Estimation

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SAR IMAGE CLASSIFICATION WITH NORMALIZED GAMMA PROCESS MIXTURES

SAR IMAGE CLASSIFICATION WITH NORMALIZED GAMMA PROCESS MIXTU...

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IEEE International Conference on Image processing

作者： Koray Kayabol Bilge Gunsel Multimedia Signal Processing and Pattern Recognition Lab. Istanbul Technical University

ISBN: (纸本)9781479923427

We propose a novel image prior for the non-parametric Bayesian mixture model based unsupervised classification of SAR images. We modified the Normalized Gamma Process prior that constitutes a more general form of the Dirichlet Process prior in order to enclose the contribution of the adjacent pixels into the classification scheme. This yields an image classification prior embedded in a mixture model that allows infinite number of clusters and enables reaching to smoothed classification maps. Based on the classification results obtained on synthetic and real TerraSAR-X images, it is shown that the proposed model is capable of accurately classifying the pixels. It applies a simple iterative update scheme at a single run without performing a hierarchical clustering strategy as used in the previously proposed methods. It is also demonstrated that the model order estimation accuracy of the proposed method outperforms the conventional finite mixture models.

关键词： Infinite mixture models Normalized gamma process mixtures Nonparametric Bayesian Image classification SAR images Image classification Mixture models images Radar polarimetry classification scheme pixel Rescue Synthetic aperture radar Specific absorption rate SAFETY ANALYSIS REPORTS mixtures

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AN ADAPTIVE TIME-FREQUENCY RESOLUTION FRAMEWORK FOR SINGLE CHANNEL SOURCE SEPARATION BASED ON NON-NEGATIVE TENSOR FACTORIZATION

AN ADAPTIVE TIME-FREQUENCY RESOLUTION FRAMEWORK FOR SINGLE C...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： S. Kirbiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Group. Dept. of Electronics and Comm. Eng. Istanbul Technical University Turkey

ISBN: (纸本)9781479903573

In this paper, we propose an adaptive time-frequency resolution based single channel sound source separation method using Non-negative Tensor Factorization (NTF). The model aims to alleviate drawbacks of working by fixed length Short Time Fourier Transform (STFT) by minimizing the smearing of signal energy in both time and frequency. A joint optimization scheme has been applied based on KL-divergence where each layer of the tensor represents the mixture at a different resolution. In order to enclose sparseness into factorization, the resynthesis is made through an adaptive weighted fusion procedure which combines the separated sources in a manner that maximizes the energy concentration. Test results reported over a large sound database indicate the introduced NTF based fusion method improves the sound quality both in terms of conventional and perceptual distortion measures.

关键词： Tensor factorization Perceptual Distortion STFT fusion procedure signal energy Resolving power Voice Quality fusion method frequency resolution

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SLEEPINESS DETECTION FROM SPEECH BY PERCEPTUAL FEATURES

SLEEPINESS DETECTION FROM SPEECH BY PERCEPTUAL FEATURES

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Bilge Gunsel Cenk Sezgin Jarek Krajewski Multimedia Signal Processing and Pattern Recognition Group Istanbul Technical Univ. Turkey Experimental Industrial Psychology Univ. of Wuppertal Germany

ISBN: (纸本)9781479903573

We propose a two-class classification scheme with a small number of features for sleepiness detection. Unlike the conventional methods that rely on the linguistics content of speech, we work with prosodic features extracted by psychoacoustic masking in spectral and temporal domain. Our features also model the variations between non-sleepy and sleepy modes in a quasi-continuum space with the help of code words learned by a bag-of-features scheme. These improve the unweighted recall rates for unseen people and minimize the language dependence. Recall rates reported based on Karolinska Sleepiness Scale (KSS) for Support Vector Machine and Learning Vector Quantization classifiers show that the developed system enables us monitoring sleepiness efficiently with a lower complexity compared to the reported benchmarking results for Sleepy Language Corpus.

关键词： Speech Sleepiness Linguistics Pragmatics Support Vector Network Retrieval recall codeword recall Mental Recall quasi-continuum learning vector quantization

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