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Incremental non-negative matrix factorization for dynamic background modelling

Incremental non-negative matrix factorization for dynamic ba...

引用

7th International Workshop on pattern recognition in Information Systems PRIS 2007;In Conjunction with ICEIS 2007

作者： Bucak, Serhat S. Gunsel, Bilge Gursoy, Ozan Multimedia Signal Processing and Pattern Recognition Lab. Dept. of Electronics and Comm. Eng. Istanbul Technical University 34469 Maslak Istanbul Turkey

ISBN: (纸本)9789728865931

In this paper, an incremental algorithm which is derived from Non-negative Matrix Factorization (NMF) is proposed for background modeling in surveillance type of video sequences. The adopted algorithm, which is called as Incremental NMF (INMF), is capable of modeling dynamic content of the surveillance video and controlling contribution of the subsequent observations to the existing representation properly. INMF preserves additive, parts-based representation, and dimension reduction capability of NMF without increasing the computational load. Test results are reported to compare background modeling performances of batch-mode and incremental NMF in surveillance type of video. Moreover, test results obtained by the incremental PCA are also given for comparison purposes. It is shown that INMF outperforms the conventional batch-mode NMF in all aspects of dynamic background modeling. Although object tracking performance of INMF and the incremental PCA are comparable, INMF is much more robust to illumination changes.

关键词： Non-negative matrix factorization

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A boosting discriminative model for moving cast shadow detection

引用

6th International Conference on Energy Minimization Methods in Computer Vision and pattern recognition, EMMCVPR 2007

作者： Yufei, Zha Ying, Chu Duyan, Bi Signal and Information Processing Lab. Engineering College of Air Force Engineering University xi'an China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

ISBN: (纸本)9783540741954

Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a boosting discriminative model for moving cast shadow detection. Firstly, color invariance subspace and texture invariance subspace are obtained by the color and texture difference between current image and background image;then, boosting is selected based on theses subspaces to discriminate cast shadow from moving objects;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields for accurate image segmentation through graph cut. Results show that the proposed method excels classical method both in indoor and outdoor scene. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Image analysis

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Video Content Representation by Incremental Non-Negative Matrix Factorization

Video Content Representation by Incremental Non-Negative Mat...

引用

IEEE International Conference on Image processing

作者： Serhat S. Bucak Bilge Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Det. of Electronics and Communication Engineering Istanbul Technical University Istanbul Turkey

Nonnegative matrix factorization (NMF) is a powerful decomposition tool which has been used in several content representation applications recently. However, there are some difficulties in implementing NMF in on-line video applications. This paper introduces an incremental NMF (INMF) without deviating from conventional NMF"s main objective function, which is minimizing the reconstruction error. The proposed algorithm is capable of modeling dynamic content of the video; thus controls contribution of the subsequent observations to the NMF representation properly. It is shown that the INMF preserves additive, parts-based representation capability of the NMF with a low computational load while offering dimension reduction. Experimental results are given to compare the reconstruction performances of the conventional and incremental NMF. In addition, video scene change detection and dynamic video content representation by INMF are investigated. Test results demonstrate that the INMF can be used as a powerful on-line factorization tool.

关键词： Matrix decomposition Layout Testing

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Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition

Maximum likelihood estimation of a reverberation model for r...

引用

European signal processing Conference (EUSIPCO)

作者： Armin Sehr Yuanhang Zheng Elmar Nöth Walter Kellermann Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Erlangen Germany Pattern Recognition (Informatik 5) University of Erlangen-Nuremberg Erlangen Germany

We propose a novel approach for estimating a reverberation model for a robust recognizer according to [1], which is designed to allow distant-talking automatic speech recognition (ASR) in reverberant environments. Based on a few calibration utterances with known transcriptions recorded in the target environment, a maximum likelihood estimator is used to find the means and variances of the reverberation model. In contrast to [1] and to HMM training on artificially reverberated training data (e. g. [2]), measurements of room impulse responses become unnecessary, and the effort for training is greatly reduced. Simulations of a connected digit recognition task show that, in highly reverberant environments, the reverberation models estimated by the proposed approach achieve significantly higher recognition rates than HMMs trained on reverberant data.

关键词： Hidden Markov models Reverberation Speech Speech recognition Maximum likelihood estimation Training Data models

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Robust Audio Watermark Decoding by Supervised Learning

Robust Audio Watermark Decoding by Supervised Learning

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： S. Kirbiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Department of Electronics and Communications Eng. Istanbul Technical University Istanbul Turkey

Most of the watermark (WM) decoding schemes use correlation-based methods because of their simplicity. In these methods, the WM signal embedded through a secret key is assumed as uncorrelated with the host signal. This is a hard restriction that can never be achieved and correlation between the received signal and the secret key becomes greater than zero even though the received signal is un-watermarked. Mostly a decision threshold specified semi-automatically is used at the decoding site. Since the audio watermarking is a nonlinear process that guarantees the inaudibility, there is no analytic way of determining an optimal threshold value that makes the WM decoding problem harder. This paper introduces a learning scheme followed by a nonlinear classification thus eliminates the threshold specification problem. The decoding process is modelled as a three-class classification problem and support vector machines (SVMs) are used in the learning of the embedded data. The decoding and detection performances of the developed system are greater than 98% and 95%, respectively. When the watermark-to-signal-ratio (WSR) is higher than -30 dB, system false alarm ratios remain less than 2%. It is shown that the introduced WM decoding method is robust to additive noise and most of add/remove and filter attacks of Stirmark

关键词： Watermarking Decoding Supervised learning Support vector machines Support vector machine classification Machine learning Meteorological radar Noise robustness Additive noise Filters

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Robust audio watermark decoding by nonlinear classification

Robust audio watermark decoding by nonlinear classification

引用

13th European signal processing Conference, EUSIPCO 2005

作者： Kirbiz, S. Yaslan, Y. Günsel, B. Multimedia Signal Processing and Pattern Recognition Lab. Dept. of Electronics and Communications Eng. Istanbul Technical University 34469 Istanbul Turkey

ISBN: (纸本)1604238216

This paper introduces an audio watermark (WM) decoding scheme that performs a Support Vector Machine (SVM) based supervised learning followed by a blind decoding. The decoding process is modelled as a two-class classification procedure. Initially, wavelet decomposition is performed on the training audio signals, and the decomposed audio frames watermarked with +1 and -1 constitute the training sets for Class 1 and Class 2, respectively. The developed system enables to extract embedded WM data at lower than -40dB Watermark-to-signal- Ratio (WSR) levels with more than 95% accuracy and it is robust to degradations including audio compression (MP3, AAC), and additive noise. It is shown that the proposed audio WM decoder eliminates the drawbacks of correlation-based methods.

关键词： Additive noise

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Content-based access to art paintings

Content-based access to art paintings

引用

IEEE International Conference on Image processing

作者： B. Gunsel S. Sariel O. Icoglu Multimedia Signal Processing and Pattern Recognition Lab Electrical-Electronics Engineering Faculty Istanbul Technical University Istanbul Turkey

This paper introduces ArtHistorian, a content-based classification and indexing system that represents the visual content of art paintings by a six-dimensional feature set. The introduced feature set is robust to scale changes and can handle variations in lighting conditions. A nonlinear SVM classifier included in the system learns the characteristics of fundamental art movements and painting styles. A hybrid classifier that combines PCA representation of paintings with the SVM classification is also exploited. It is shown that ArtHistorian is capable of classifying art paintings based on painters as well as art movements with an accuracy of greater than 90% and its false alarm ratio is very small. The developed system enables the user to run content-based queries and to retrieve from painting databases created in XML format.

关键词： Art Painting Support vector machines Support vector machine classification Indexing Robustness Principal component analysis Information retrieval Content based retrieval Databases

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Classification and indexing of paintings based on art movements 12

Classification and indexing of paintings based on art moveme...

引用

12th European signal processing Conference, EUSIPCO 2004

作者： Icoglu, Oguz Gunsel, Bilge Sariel, Sanem Multimedia Signal Processing and Pattern Recognition Lab. Electrical-Electronics Eng. Faculty Istanbul Technical University Maslak-Istanbul34469 Turkey Vienna University of Technology Building Physics and Human Ecology Institute Austria

ISBN: (纸本)9783200001657

This paper outlines the automatic extraction of features of paintings' art movements such as classicism, impressionism and cubism;and introduces a system developed for the classification and indexing of paintings based on their art movements. A six dimensional feature set is proposed for the representation of content and it is shown that the feature set enables to highlight art movements efficiently. In the classifier design, statistical pattern recognition approach is exploited and Bayesian, k-NN and SVM classifiers are employed. A classification accuracy of over 90% is achieved with very small false alarm ratios while the lowest performance is obtained by the k-NN. System also offers a quick query based database search by indexing the paintings with their six dimensional feature vectors, and provides an applicable program for museums and exhibition centres. © 2004 EUSIPCO.

关键词： Query processing

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An integrated decoding framework for audio watermark extraction

An integrated decoding framework for audio watermark extract...

引用

International Conference on pattern recognition

作者： Y. Yaslan B. Gunsel Multimedia Signal Processing and Pattern Recognition Lab Electrical-Electronics Faculty. Department of Electronics and Communications Engineering Istanbul Technical University Maslak Istanbul Turkey

This paper proposes a blind audio watermark extraction technique that allows performing watermark decoding while installing data synchronization. The proposed decoding algorithm employs correlation techniques supported by a wavelet denoising process, thus improves the decoding performance significantly. A data adaptive nonlinear MPEG Layer 1 Model 1 compatible watermark encoder is designed for watermark embedding. A channel encoder is also included into the system to take the advantage of error correction. The method does not require the original audio for decoding and it is robust to channel noise, filtering as well as stereo-to-mono conversions. It allows working at very low watermark-to-signal ratios thus preserves inaudibility.

关键词： Watermarking Data mining Noise reduction signal processing pattern recognition Noise robustness Communication channels Meteorological radar Iterative decoding signal processing algorithms

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Blind audio watermark decoding using independent component analysis

Blind audio watermark decoding using independent component a...

引用

International Conference on pattern recognition

作者： S. Sener B. Gunsel Institute of Informatics Satellite Communication and Remote Sensing Istanbul Technical University Maslak Istanbul Turkey Multimedia Signal Processing and Pattern Recognition Laboratory Electrical-Electronics Faculty. Department of Electronics and Communications Engineering Istanbul Technical University Maslak Istanbul Turkey

This paper proposes an audio watermark extraction technique which adopts independent component analysis (ICA) for blind watermark decoding. Unlike the existing work, our method allows to combine data synchronization and watermark decoding into one optimization procedure, thus robust to transmission over different channels. Watermark encoder is designed as a nonlinear data embedding machine which is compatible to MPEG Layer 1 Model 1. It is shown that the proposed ICA based watermark decoding scheme allows decoding watermark info accurately even though the watermark to signal ratio is less than -20 dB. The method is robust to stereo-to-mono conversions and performs very well when the channel noise level is high.

关键词： Watermarking Independent component analysis Noise robustness Iterative decoding Frequency synchronization signal processing pattern recognition Protection Frequency domain analysis signal to noise ratio

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