We present a method for clustering data streams incrementally, designed to discover all valid density peaks in a single pass, in a non-parametric fashion. It detects emerging clusters along the stream by dynamically locating kernels in the most promising areas and performing a Stochastic Mean Shift procedure to find clustering centers. We present a density estimation approach for dynamic initialization, considering every sub-stream that follows `emerging data' as a sample set and applying Hypothesis Testing (p-value approach) to estimate its local density. The sub-stream size and the p-value are determined in a way that provides provable accuracy guarantee. We compare our method with the state-of-the-art, on realistic and complex datasets. We show that it outperforms not only stream algorithms but also their more complex, non-stream foundational paradigms.

We present a fast and accurate center-based, singlepass clustering method, with main focus on improving the trade-off between accuracy and speed in computer vision problems, such as creating visual vocabularies. We use a stochastic Mean-shift procedure to seek the local density peaks within a single pass of the data. We also present a dynamic kernel generation along with a density test procedure that finds the most promising kernel initializations. In our algorithm, we use two data structures, namely a dictionary of permanent kernels, and a 'short memory' that is used to determine emerging kernels to be maintained and outliers to be discarded. In our experiments we make extensive comparisons with popular clustering algorithms, with respect to accuracy and efficiency.

Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters derived by the image segmentation procedure. Existing methods, including likelihood- or entropy-based criteria, and reversible Markov chain Monte Carlo methods, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (DP, infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori; infinite mixture models based on the original DP or spatially constrained variants of it have been applied in unsupervised image segmentation applications showing promising results. Under this motivation, to resolve the aforementioned issues of HMRF models, in this paper, we introduce a nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model, formulated on the basis of a joint Dirichlet process mixture (DPM) and Markov random field (MRF) construction. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally demonstrate its advantages over competing methodologies.

Dirichlet process (DP) mixture models have recently emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Hidden Markov random field (HMRF) models are parametric statistical models widely used for image segmentation, as they appear naturally in problems where a spatially-constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e. the number of segments derived by the image segmentation procedure. Typically, for this purpose, various likelihood based criteria are employed. Nevertheless, such methods often fail to yield satisfactory results, exhibiting significant overfitting proneness. Recently, higher order conditional random field models using potentials defined on superpixels have been considered as alternatives tackling these issues. Still, these models are in general computationally inefficient, a fact that limits their widespread adoption in practical applications. To resolve these issues, in this paper we introduce a novel, nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model. We describe an efficient variational Bayesian inference algorithm for the proposed model, and we apply it to a series of image segmentation problems, demonstrating its advantages over existing methodologies.

We present a hybrid framework for integrating deformable models with learning-based classification, for image segmentation with region ambiguities. We show how a region-based geometric model is coupled with conditional random fields (CRF) in a simple graphical model, such that the model evolution is driven by a dynamically updated probability field. We define the model shape with the signed distance function, while we formulate the internal energy with a C(1) continuity constraint, a shape prior, and a term that forces the zero level of the shape function towards a connected form. The latter can be seen as a term that forces different closed curves on the image plane to merge, and, therefore, our model inherently carries the property of merging regions. We calculate the image likelihood that drives the evolution using a collaborative formulation of conditional random fields (CoCRF), which is updated during the evolution in an online learning manner. The CoCRF infers class posteriors to regions with feature ambiguities by assessing the joint appearance of neighboring sites, and using the classification confidence to regulate the inference. The novelties of our approach are (i) the tight coupling of deformable models with classification, combining the estimation of smooth region boundaries with the robustness of the probabilistic region classification, (ii) the handling of feature variations, by updating the region statistics in an online learning manner, and (iii) the improvement of the region classification using our CoCRF. We demonstrate the performance of our method in a variety of images with clutter, region inhomogeneities, boundary ambiguities, and complex textures, from the zebra and cheetah examples to medical images.

Transportation and border security systems have a common goal: to allow law-abiding people to pass through security and detain those people who intend to harm. Understanding how intention is concealed and how it might be detected should help in attaining this goal. In this paper, we introduce a multidisciplinary theoretical model of intent concealment along with three verbal and nonverbal automated methods for detecting intent: message feature mining, speech act profiling, and kinesic analysis. This paper also reviews a program of empirical research supporting this model, including several previously published studies and the results of a proof-of-concept study. These studies support the model by showing that aspects of intent can be detected at a rate that is higher than chance. Finally, this paper discusses the implications of these findings in an airport-screening scenario.

We present a topology independent solution for segmenting objects with texture patterns of any scale, using an implicit deformable model driven by conditional random fields (CRFs). Our model integrates region and edge information as image driven terms, whereas the probabilistic shape and internal (smoothness) terms use representations similar to the level-set based methods. The evolution of the model is solved as a MAP estimation problem, where the target conditional probability is decomposed into the internal term and the image-driven term. For the later, we use discriminative CRFs in two scales, pixel- and patch-based, to obtain smooth probability fields based on the corresponding image features. The advantages and novelties of our approach are (i) the integration of CRFs with implicit deformable models in a tightly coupled scheme, (ii) the use of CRFs which avoids ambiguities in the probability fields, (iii) the handling of local feature variations by updating the model interior statistics and processing at different spatial scales, and (v) the independence from the topology. We demonstrate the performance of our method in a wide variety of images, from the zebra and cheetah examples to the left and right ventricles in cardiac images.

In this paper we present a hybrid probabilistic framework for 3D image segmentation, using Conditional Random Fields (CRFs) and implicit deformable models. Our 3D deformable model uses voxel intensity and higher scale textures as data-driven terms, while the shape is formulated implicitly using the Euclidean distance transform. The data-driven terms are used as observations in a 3D discriminative CRF, which drives the model evolution based on a simple graphical model. In this way, we solve the model evolution as a joint MAP estimation problem for the 3D label field of the CRF and the 3D shape of the deformable model. We demonstrate the performance of our approach in the estimation of the volume of the human tear menisci from images obtained with optical coherence tomography.

The dynamic variation of the human tear meniscus (tears around the eye lids) is very critical in visual function, maintenance of corneal integrity, and ocular comfort. The quantitative measuring of the tear menisci around the eyelids is though a challenging task. In our work, tear meniscus images are obtained with our custom-built optical coherence tomography (OCT) and are processed using our novel segmentation method. For the latter, we use an implicit deformable model driven by a conditional random field (CRF). The evolution of the model is solved as MAP estimation. The target conditional probability is decomposed using a simple graphical model, where the probability field of the pixel labels given the image observations is estimated using a discriminative CRF. Our results show that our segmentation approach successfully handles clutter and boundary ambiguities of the tear menisci, which makes our integrated system reliable for the every day medical practice.

Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. This research effort attempts to leverage automated systems to augment humans in detecting deception by analyzing nonverbal behavior on video. By tracking faces and hands of an individual, it is anticipated that objective behavioral indicators of deception can be isolated, extracted and synthesized to create a more accurate means for detecting human deception. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. A proof-of-concept study is presented that uses Blob analysis to extract visual cues and events, throughout the examined videos. The integration of these cues is done using a hierarchical hidden Markov model to explore behavioral state identification in the detection of deception, mainly involving the detection of agitated and over-controlled behaviors

Knowledge-based fuzzy inference and neural learning are used in this paper in order to model the event recognition task in semantic video analysis. The advantage of their use is the symbolic nature of the representation of the knowledge concerning the events to be recognized. Moreover, this knowledge can be adapted with the aid of data taken from video sequences. The proposed system has been tested in soccer video sequences for detecting some complex predetermined (and represented in the form of rules) events.

Efficient moving object tracking in cases of partial occlusion is a challenging task for the researchers in the fields of computer vision and video processing. In modern coding standards, like MPEG-4 and MPEG-7, the term of video objects is used to define moving objects in a video sequence. Automatic extraction of such objects is by no means trivial, and occlusion is one of the most important problems. This paper reformulates one of the most popular deformable templates for shape modeling and object tracking, the Snakes, in a probabilistic manner, in order to include providence for partial occlusion of the moving objects. Experiments of object tracking in partial occlusion, in complex natural sequences, where temporal clutter, abrupt motion and external lightning changes have been carried out, showing the efficiency of the proposed approach.