Bayesian segmentation of hyperspectral images

In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results.Comment: 8 pages, 2 figures, presented at MaxEnt 2004, Inst. Max Planck, Garching, German

Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm

Advanced Driving Assistance Prediction Systems

Future automobiles are going to experience a fundamental evolution by installing semiotic predictor driver assistance equipment. To meet these equipment, Continuous driving-behavioral data have to be observed and processed to construct powerful predictive driving assistants. In this thesis, we focus on raw driving-behavioral data and present a prediction method which is able to prognosticate the next driving-behavioral state. This method has been constructed based on the unsupervised double articulation analyzer method (DAA) which is able to segment meaningless continuous driving-behavioral data into a meaningful sequence of driving situations. Thereafter, our novel model by mining the sequences of driving situations can define and process the most influential data parameters. After that, our model by utilizing these parameters can interpret the dynamic driving data and predict the next state of the determined vehicle. Proficiency of this model has been evaluated using over three terabytes driving behavioral data which include 16 drivers’ data, totally for more than 17 hours and over 456 Km

Bayesian Hidden Markov Modeling of Array CGH Data

Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Since the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme and breast cancer are analyzed, and comparisons are made with some widely-used algorithms to illustrate the reliability and success of the technique

Activity recognition using a supervised non-parametric hierarchical HMM

The problem of classifying human activities occurring in depth image sequences is addressed. The 3D joint positions of a human skeleton and the local depth image pattern around these joint positions define the features. A two level hierarchical Hidden Markov Model (H-HMM), with independent Markov chains for the joint positions and depth image pattern, is used to model the features. The states corresponding to the H-HMM bottom level characterize the granular poses while the top level characterizes the coarser actions associated with the activities. Further, the H-HMM is based on a Hierarchical Dirichlet Process (HDP), and is fully non-parametric with the number of pose and action states inferred automatically from data. This is a significant advantage over classical HMM and its extensions. In order to perform classification, the relationships between the actions and the activity labels are captured using multinomial logistic regression. The proposed inference procedure ensures alignment of actions from activities with similar labels. Our construction enables information sharing, allows incorporation of unlabelled examples and provides a flexible factorized representation to include multiple data channels. Experiments with multiple real world datasets show the efficacy of our classification approach

Ensemble learning method for hidden markov models.

For complex classification systems, data are gathered from various sources and potentially have different representations. Thus, data may have large intra-class variations. In fact, modeling each data class with a single model might lead to poor generalization. The classification error can be more severe for temporal data where each sample is represented by a sequence of observations. Thus, there is a need for building a classification system that takes into account the variations within each class in the data. This dissertation introduces an ensemble learning method for temporal data that uses a mixture of Hidden Markov Model (HMM) classifiers. We hypothesize that the data are generated by K models, each of which reacts a particular trend in the data. Model identification could be achieved through clustering in the feature space or in the parameters space. However, this approach is inappropriate in the context of sequential data. The proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the N individual sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE) based discriminative, and the Variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the multiple models outputs using a decision level fusion method such as an artificial neural network or a hierarchical mixture of experts. Our approach was evaluated on two real-world applications: (1) identification of Cardio-Pulmonary Resuscitation (CPR) scenes in video simulating medical crises; and (2) landmine detection using Ground Penetrating Radar (GPR). Results on both applications show that the proposed method can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data