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Multimedia Tools Appl. 01/2012; 57:131-144.
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ABSTRACT: In this paper we present a novel conditional random field (CRF) model based on Laplacian mixtures for image labeling. Nature images posses many spatial regularities that can be efficiently modeled by probabilistic graphical models such as CRF. Usually hundreds of features and several types of feature functions are used together which increases computational complexity and makes the training difficult to converge. We propose a new Laplacian mixture CRF model, which simplifies the training and inference process without losing labeling accuracy. The belief propagation inference and stochastic gradient descent training are formulated accordingly for the new model. The experimental results demonstrate that the new approach achieves better classification accuracy than the baseline CRF and comparable results with the state-of-the-art complex models.
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010 · 4.63 Impact Factor
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ABSTRACT: In this paper, a new hidden conditional random field (HCRF) model with independent component analysis (ICA) mixture feature functions is developed for sports event classification. Unlike Hidden Markov Model (HMM), HCRF is a discriminative model without conditional independence assumption of observations, which is more suitable for video content analysis. According to the non-Gaussian property of sports event features, a new feature function using the likelihood of ICA mixture component is proposed to further enhance the HCRF model. The discriminant power of HCRF and representation power of ICA mixture for non-Gaussian distribution are combined. The new model is applied to challenging bowling and golf event classification. The simulation results prove our analysis that the new ICA mixture HCRF outperforms the existing mixture HMM models in term of classification accuracy.
Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on; 11/2009
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ABSTRACT: In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel labeling using MRF. But the local interaction in MRF is limited to pixel label comparison. We propose a new localized superpixel Markov random field (SMRF) model to incorporate local data interaction in unsupervised parameter learning. The advantages of the new model include computational efficiency by using superpixel structure and its ability to integrate local knowledge in the learning process. Quantitative evaluation and visual effects show that the new model achieves not only better segmentation accuracy but also lower computational cost than the baseline pixel based model.
Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on; 08/2009
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ABSTRACT: Look-up table (LUT)-based data hiding is a simple and efficient technique to hide secondary information (watermark) into multimedia work for various applications such as copyright protection, transaction tracking or content annotation. This paper studies the distortion introduced by a general LUT-based data hiding. We find that designing LUT according to the distribution of host data and watermark data can greatly reduce the distortion of LUT embedding. A new practical reduced-distortion LUT design method is developed for robust data hiding. The new method is applied in a wavelet domain image data hiding system and only significant wavelet coefficients are used to embed the watermark. A Gaussian mixture model and a related expectation-maximization algorithm-based method are employed to model the statistical distribution of the host image. The statistical model is used to select significant coefficients of the host image for data hiding. The experimental results show that compared to the conventional odd-even LUT embedding method, the presented new LUT data hiding algorithm provides average 1. 5-2. 5 dB PSNR improvement and better robustness for image watermarking.
IEEE Transactions on Circuits and Systems for Video Technology 07/2008; · 1.65 Impact Factor
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Proceedings of the 7th ACM International Conference on Image and Video Retrieval, CIVR 2008, Niagara Falls, Canada, July 7-9, 2008; 01/2008
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Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2008, March 30 - April 4, 2008, Caesars Palace, Las Vegas, Nevada, USA; 01/2008
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ABSTRACT: Information theory guides us to investigate the choice of quantizers for data hiding applications. In this paper, the design of the quantizer selection rule in trellis coded quantization (TCQ) based data hiding is discussed. A novel trellis branch quantizer selection rule which changes the old state transition function and takes advantage of all trellis states is proposed so as to increase robustness against attacks. Theoretical analysis and simulation results show that the new TCQ path selection (or branch selection) method achieves better bit error rate (BER) performance in the case of Gaussian attack compared to other popular approaches. The path selection rule could also be used as a secret key to provide security for the practical data hiding
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on; 05/2007 · 4.63 Impact Factor
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Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, ICME 2006, July 9-12 2006, Toronto, Ontario, Canada; 01/2006
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ABSTRACT: In this paper we present a new conditional random field (CRF) model based on Gaussian mixture potentials for indoor image labeling, which is useful in interactive room decoration system. Indoor images which posses many spatial regularities can be efficiently modeled by probabilistic graphical models such as CRF. The potential functions in CRF are usually set empirically and differently for different features depending on applications. We propose a new CRF model based on a general Gaussian mixture potential for different group of features, which has the advantage of labeling accuracy and training simplicity. The new model with belief propagation inference and stochastic gradient descent training is applied to floor region labeling of indoor images in Labelme database. Simulation results and visual effects prove our analysis. Comparing to other CRF models the new approach is more efficient for indoor image labeling tasks.
