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IEEE Conference on Computer Vision and pattern recognition

作者： Junjie Yan Yinan Yu Xiangyu Zhu Zhen Lei Stan Z. Li Nat. Lab. of Pattern Recognition China

ISBN: (纸本)9781467369657

Object detection is often conducted by object proposal generation and classification sequentially. This paper handles object detection in a superpixel oriented manner instead of the proposal oriented. Specially, this paper takes object detection as a multi-lab.l superpixel lab.ling problem by minimizing an energy function. It uses the data cost term to capture the appearance, smooth cost term to encode the spatial context and lab.l cost term to favor compact detection. The data cost is learned through a convolutional neural network and the parameters in the lab.ling model are learned through a structural SVM. Compared with proposal generation and classification based methods, the proposed superpixel lab.ling method can nat.rally detect objects missed by proposal generation step and capture the global image context to infer the overlapping objects. The proposed method shows its advantage in Pascal VOC and ImageNet. Notably, it performs better than the ImageNet ILSVRC2014 winner GoogLeNet (45.0% V.S. 43.9% in mAP) with much shallower and fewer CNNs.

关键词： Object detection spatial context

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Estimate articulatory MRI series from acoustic signal using deep architecture

Estimate articulatory MRI series from acoustic signal using ...

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IEEE Internat.onal Conference on Acoustics, Speech and Signal Processing

作者： H. Li J. Tao M. Yang B. Liu Nat. Lab. of Pattern Recognition Inst. of Autom. Beijing China

ISBN: (纸本)9781467369985

This paper presents our work on acoustic-to-articulatory inversion mapping, in which, the articulatory data is the MRI series for articulators on mid-sagittal plan. Deep architectures based on restricted Boltzmann machine (RBM) and linear regression are employed to construct the audio-visual mapping. We test two architectures to initialize the neural network: the bottom-up stacked RBM with top regression layer architecture and the one with extra Gaussian-Bernoulli RBM on the top of the former architecture. GMM-based mapping is used as baseline method. The MRI data from USC-TIMIT database is used for the training. The experimental results show that the deep regression network is an effective model to construct the mapping from acoustic speech signal to articulatory MRI series, and also indicate that it is a better strategy to initial the top layer as Gaussian-Bernoulli RBM to compress the MRI data before the liner regression.

关键词： MRI acoustic-to-articulatory inversion deep neural network deep regression network Magnetic Resonance Imaging C7orf49 gene audio signals ARCHITECTURE Voice Signal MAPPING(MATHEMATICAL) Articulators baseline method Building construction top layer series (mathematics)

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WINDOW MINING BY CLUSTERING MID-LEVEL REPRESENTATION FOR WEAKLY SUPERVISED OBJECT DETECTION

WINDOW MINING BY CLUSTERING MID-LEVEL REPRESENTATION FOR WEA...

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IEEE Internat.onal Conference on Image Processing

作者： Chong Wang Weiqiang Ren Kaiqi Huang Nat. Lab. of Pattern Recognition Inst. of Autom. Beijing China

ISBN: (纸本)9781479957521

Discovering positive detection windows in training images is a challenging problem in weakly supervised object detection. In this paper, we propose a window mining strategy by the simple and efficient k-means clustering. Firstly, a recent segmentation based object proposal is used for its highly semantic candidate windows;secondly, the bag-of-words model is adopted as mid-level object representation for each window. By clustering these windows with k-means, semantic clusters can be generated. Then, to discover the positive windows from these clusters, we further propose a cluster selection method based on each cluster's discriminat.on, which is evaluated by classification performance given the category lab.l. With the semantic clusters, this selection process is effective and efficient. Evaluation on the challenging PASCAL VOC 2007 dataset shows that the proposed method outperforms all previous weakly supervised approaches.

关键词： data mining image representation image segmentation learning (artificial intelligence) object detection pattern clustering PASCAL VOC 2007 dataset bag-of-words model cluster selection method detection windows discovery k-means clustering mid-level object representation mid-level representation clustering segmentation based object proposal weakly supervised object detection window mining strategy Feature extraction Motorcycles Object detection Proposals Semantics Training Visualization k-means weakly supervised learning window mining

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Quantized Convolutional Neural Networks for Mobile Devices

Quantized Convolutional Neural Networks for Mobile Devices

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IEEE Conference on Computer Vision and pattern recognition

作者： Jiaxiang Wu Cong Leng Yuhang Wang Qinghao Hu Jian Cheng Nat. Lab. of Pattern Recognition Inst. of Autom. Beijing China

ISBN: (纸本)9781467388528

Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4 ～ 6× speed-up and 15 ～ 20× compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.

关键词： Quantization (signal) Convolutional codes Kernel Computational modeling Estimation error Acceleration Image coding

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Ordinal pyramid pooling for rotation invariant object recognition

Ordinal pyramid pooling for rotation invariant object recogn...

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IEEE Internat.onal Conference on Acoustics, Speech and Signal Processing

作者： G. Wang B. Fan C. Pan Nat. Lab. of Pattern Recognition Inst. of Autom. Beijing China

ISBN: (纸本)9781467369985

Local feature descriptor plays a fundamental role in many visual tasks, and its rotation invariance is a key issue for many recognition and detection problems. This paper proposes a novel rotation invariant descriptor by ordinal pyramid pooling of local Fourier transform features based on their radial gradient orientations. Since both the low-level feature and pooling strategy are rotation invariant, the obtained descriptor is rotation invariant by nat.re. Pooling based on orders of gradient orientations is not only invariant to in-plane rotation, but also encodes gradient orientation information into descriptor as well as spatial information to some extent. Moreover, these information is enhanced by the proposed pyramid pooling structure. Therefore, our method is nat.rally rotation invariant and has strong discriminat.ve ability. Experimental results on the aerial car dataset demonstrate the effectiveness of our descriptor.

关键词： Local feature descriptor Orders of radial gradient orientations Ordinal pyramid Rotation invariant pooling Descriptors Invariant Pyramid Plane rotation VISUAL TASKS Ordinal

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A NON-PARAMETER BAYESIAN CLASSIFIER FOR FACE recognition

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Journal of Electronics(China) 2003年第5期20卷 362-370页

作者： Liu Qingshan Lu Hanqing Ma Songde (nat. lab.of pattern recognition, Inst. of Automation, Chinese Academy of Sciences, Beijing 100080) Nat. Lab of Pattern Recognition Inst. of Automation Chinese Academy of Sciences Beijing

A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.

关键词： Kernel Density Estimation (KDE) Probabilistic Reasoning Models (PRM) Principal Component Analysis (PCA) Kernel-based PCA (KPCA) Face recognition

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Towards a description for video indexation

Towards a description for video indexation

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Internat.onal Conference on pattern recognition

作者： F. Lebourgeois J.-M. Jolion P.Ch. Awart Pattern Recognition & Vision Lab. Inst. Nat. des Sci. Appliquees Villeurbanne France

This paper presents the overall scheme of an indexation system for broadcast video designed for very large databases. We discuss features which can possibly be extracted from a video sequence so as to be used for "queries by example". We present examples of semantic features (query by content) like text reading, face localisation and classification. As this study is still at its infancy, we point out the key features and temporary achievements and present some temporary results.

关键词： Video sequences lab.ratories Multimedia communication Broadcasting Indexing Image storage Image color analysis pattern recognition Design automation Tellurium

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Two plane camera calibration: a unified model

Two plane camera calibration: a unified model

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Conference on Computer Vision and pattern recognition (CVPR)

作者： G.-Q. Wei S.D. Ma National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Nat. Lab. of Pattern Recognition Chinese Acad. of Sci. Beijing China

A unified model for camera calibration is presented. The model does not assume any explicit form of imaging process and can perform linear back projection while considering all lens distortions. This model is well suited to stereo reconstruction. It has been shown that the original two-plane model, the linear and nonlinear pin hole model, can all be realized as special cases of this model. With the two-plane model, optimal orientation of the two reference planes can be found intuitively.< >

关键词： Cameras Calibration Interpolation Lenses Nonlinear distortion Computer vision Spline Image storage pattern recognition Automation

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An effective algorithm for fingerprint matching

An effective algorithm for fingerprint matching

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IEEE Region 10 Internat.onal Conference TENCON

作者： Ying Ha Heniu Tan Yunhong Wang Nat. Lab of Pattem Recognition Inst. of Autornafion Beijing China National Lab of Pattern Recognition Institute of Automation Beijing China

ISBN: (纸本)0780374908

Fingerprint matching is one of the most important stages in automatic fingerprint identification systems (AFIS). Traditional methods treat this problem as point pattern matching, which is essentially an intractable problem due to the various linear deformations commonly observed in fingerprint images. We propose an effective fingerprint matching algorithm based on error propagation. Firstly, ridge information and Hough transformation are adopted to find several pairs of matching minutiae, the initial correspondences, which are used to estimate the common region of two fingerprints and the alignment parameters. Then a matched set which includes the correspondence and its surrounding matched minutiae pairs is established. The subsequent matching process is guided by the concept of error propagation: the matching errors of each unmatched minutiae are estimated according to those of its most relevant neighbor minutiae. In order to prevent the process from being misguided by mismatched minutiae pairs, we adopt a flexible propagation scheme. Experimental results demonstrate the robustness of our algorithm to non-linear deformation.

关键词： Fingerprint recognition pattern matching Robustness Fingers Image matching pattern recognition Content addressable storage Automation Image databases Spatial databases

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Prosodic word prediction using the lexical information

Prosodic word prediction using the lexical information

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IEEE Internat.onal Conference on nat.ral Language Processing and Knowledge Engineering (NLP-KE)

作者： Honghui Dong JianhuaTao Bo Xu National Laboratory of Pattern Recognition China Nat. Lab. of Pattern Recognition Chinese Acad. of Sci. Beijing China High Technology Innovation Center Institute of Automation Chinese Academy of Science China

As a basic prosodic unit, the prosodic word influences the nat.ralness and the intelligibility greatly. Although the relation between the lexicon word and the prosodic word has been widely discussed, the prosodic word prediction still cannot reach a high precision and recall. In this paper, the study shows the lexical features are more efficient in prosodic word prediction. Based on careful analysis on the mapping relationship and the difference between the lexicon words and the prosodic words, this paper proposes two methods to predict prosodic words from the lexicon words sequence. The first is a statistic probability model, which efficiently combines the local POS and word length information. Experiments show that by choosing appropriate threshold this statistic model can reach a high precision and high recall ratio. Another is an SVM-based method. In this SVM classifier, an efficient feature is introduced. Besides the POS and the word length features, the in-word-probability (IWP) is used. IWP means the probability of a lexicon word to be in a prosodic word. The precision and the recall ratio are improved greatly after using SVM classifier and the IWP feature.

关键词： Support vector machines Support vector machine classification Statistics Probability Speech synthesis Predictive models lab.ratories pattern recognition Technological innovation Automation

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