Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classification problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.

title: Man-made structure detection in natural images using a causal multiscale random field
abstract: This paper presents a generative model based approach to man-made structure detection in 2D (two-dimensional) natural images. The proposed approach uses a causal multiscale random field suggested by Bouman and Shapiro (1994) as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multi-scale features is presented that captures the general statistical properties of man-made structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on real-world images from the Corel data set, and a performance comparison with other techniques is presented.

title: Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
abstract: Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we present an approach for approximate maximum likelihood parameter learning in discriminative field models, which is based on approximating true expectations with simple piecewise constant functions constructed using inference techniques. Gradient ascent with these updates exhibits compelling limit cycle behavior which is tied closely to the number of errors made during inference. The performance of various approximations was evaluated with different inference techniques showing that the learned parameters lead to good classification performance so long as the method used for approximating the gradient is consistent with the inference mechanism. The proposed approach is general enough to be used for the training of, e.g., smoothing parameters of conventional Markov Random Fields (MRFs).

title: A hierarchical field framework for unified context-based classification
abstract: We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented

title: Discriminative random fields: a discriminative framework for contextual interaction in classification
abstract: In this work we present discriminative random fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data. The discriminative random fields offer several advantages over the conventional Markov random field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used in the MRF framework. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where likelihood parameters are usually learned separately from the field parameters. We illustrate the advantages of the DRFs over the MRF framework in an application of man-made structure detection in natural images taken from the Corel database.

title: An observation-constrained generative approach for probabilistic classification of image regions
abstract: In this paper, we propose a probabilistic region classification scheme for natural scene images. In conventional generative methods, a generative model is learnt for each class using all the available training data belonging to that class. However, if an input image has been generated from only a subset of the model support, use of the full model to assign generative probabilities can produce serious artifacts in the probability assignments. This problem arises mainly when the different classes have multimodal distributions with considerable overlap in the feature space. We propose an approach to constrain the class generative probability of a set of newly observed data by exploiting the distribution of the new data itself and using linear weighted mixing. A Kullback–Leibler Divergence-based fast model selection procedure is also proposed for learning mixture models in a low dimensional feature space. The preliminary results on the natural scene images support the effectiveness of the proposed approach.

title: Recognition of human actions using moment based features and artificial neural networks
abstract: This paper presents performance of view-based approach in automated recognition of predefined hand and gross body actions using artificial neural network. This approach represents motion by a static grey scale image template computed by collapsing the temporal components into the cumulative image-difference of frames. The seven invariant Hu moments are used as the feature vectors. The performance of the system is tested in real time using feed forward multilayer perceptron (MLP) based on back propagation.

title: Barrier coverage with wireless sensors
abstract: In old times, castles were surrounded by moats (deep trenches filled with water, and even alligators) to thwart or discourage intrusion attempts. One can now replace such barriers with stealthy and wireless sensors. In this paper, we develop theoretical foundations for laying barriers of wireless sensors. We define the notion of k-barrier coverage of a belt region using wireless sensors. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether a region is k-barrier covered. Next, we establish the optimal deployment pattern to achieve k-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. We introduce two notions of probabilistic barrier coverage in a belt region -- weak and strong barrier coverage. While weak barrier-coverage with high probability guarantees the detection of intruders as they cross a barrier of stealthy sensors, a sensor network providing strong barrier-coverage with high probability (at the expense of more sensors) guarantees the detection of all intruders crossing a barrier of sensors, even when the sensors are not stealthy. Both types of barrier coverage require significantly less number of sensors than full-coverage, where every point in the region needs to be covered. We derive critical conditions for weak k-barrier coverage, using which one can compute the minimum number of sensors needed to provide weak k-barrier coverage with high probability in a given belt region. Deriving critical conditions for strong k-barrier coverage for a belt region is still an open problem.

title: A Weighted Finite State Transducer Implementation of the Alignment Template Model for Statistical Machine Translation
abstract: We present a derivation of the alignment template model for statistical machine translation and an implementation of the model using weighted finite state transducers. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard FSM operations involving these transducers. One of the benefits of using this framework is that it obviates the need to develop specialized search procedures, even for the generation of lattices or N-Best lists of bitext word alignments and translation hypotheses. We evaluate the implementation of the model on the French-to-English Hansards task and report alignment and translation performance.

title: Hierarchical back-face computation
abstract: We present a sub-linear algorithm to compute the set of back-facing polygons in a polyhedral model. The algorithm partitions the model into hierarchical clusters based on the orientations and positions of the polygons. As a pre-processing step, the algorithm constructs spatial decompositions with respect to each cluster. For a sequence of back-face computations, the algorithm exploits the coherence in view-point movement to efficiently determine whether it is in front of or behind a cluster. Due to coherence, the algorithm's expected running time is linear in the number of clusters on average. We have applied this algorithm to speed up the rendering of polyhedral models. On average, we are able to cull about 40% of the polygons. The algorithm accounts for 5% of the total CPU time per frame on an SGI Onyx. The overall frame rate is improved by 40–75% as compared to the standard back-face culling implemented in hardware. We also present an extension of our back-face computation algorithm to determine silhouettes of polygonal models. Our technique finds true perspective silhouettes by collecting edges at the common boundaries of back-facing and front-facing clusters.

title: Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
abstract: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 ) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.