Tracking Atoms with Particles

We present a general framework and an efficient algorithm for tracking relevant video structures. The structures to be tracked are implicitly defined by a Matching Pursuit procedure that extracts and ranks the most important image contours. Based on the ranking, the contours are automatically selected to initialize a Particle Filtering tracker. The proposed algorithm deals with salient video entities whose behavior has an intuitive meaning, related to the physics of the signal. Moreover, as the interactions between such structures are easily defined, the inference of higher level signal configurations can be made intuitive. The proposed algorithm improves the performance of existing video structures trackers, while reducing the computational complexity. The algorithm is demonstrated on audiovisual source localization

Analysis of Multimodal Sequences Using Geometric Video Representations

This paper presents a novel method to correlate audio and visual data generated by the same physical phenomenon, based on sparse geometric representation of video sequences. The video signal is modeled as a sum of geometric primitives evolving through time, that jointly describe the geometric and motion content of the scene. The displacement through time of relevant visual features, like the mouth of a speaker, can thus be compared with the evolution of an audio feature to assess the correspondence between acoustic and visual signals. Experiments show that the proposed approach allows to detect and track the speaker's mouth when several persons are present on the scene, in presence of distracting motion, and without prior face or mouth detection

Extraction of Audio Features Specific to Speech using Information Theory and Differential Evolution

We present a method that exploits an information theoretic framework to extract optimized audio features using the video information. A simple measure of mutual information (MI) between the resulting audio features and the video ones allows to detect the active speaker among different candidates. Our method involves the optimization of an MI-based objective function. No approximation is introduced to solve this optimization problem, neither concerning the estimation of the probability density functions (pdf) of the features, nor the cost function itself. The pdf are estimated from the samples using a non-parametric approach. As far as concern the optimization process itself, three different optimization methods (one local and two globals) are compared in this paper. The Differential Evolution algorithm is shown to be outstanding performant for our problem and is threrefore eventually retains. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speeh is discussed. As a result, our method achieves a speaker detection rate of 100% on our test sequences, and of 95% on a state-of-the-art sequence

Blind Audio-Visual Source Separation Using Sparse Redundant Representations

This report presents a new method to confront the Blind Audio Source Separation (BASS) problem, by means of audio and visual information. In a given mixture, we are able to locate the video sources first and, posteriorly, recover each source signal, only with one microphone and the associated video. The proposed model is based on the Matching Pursuit (MP) [18] decomposition of both audio and video signals into meaningful structures. Frequency components are extracted from the soundtrack, with the consequent information about energy content in the time-frequency plane of a sound. Moreover, the MP decomposition of the audio is robust in front of noise, because of its plain characteristic in this plane. Concerning the video, the temporal displacement of geometric features means movement in the image. If temporally close to an audio event, this feature points out the video structure which has generated this sound. The method we present links audio and visual structures (atoms) according to their temporal proximity, building audiovisual relationships. Video sources are identified and located in the image exploiting these connections, using a clustering algorithm that rewards video features most frequently related to audio in the whole sequence. The goal of BASS is also achieved considering the audiovisual relationships. First, the video structures close to a source are classified as belonging to it. Then, our method assigns the audio atoms according to the source of the video features related. At this point, the separation performed with the audio reconstruction is still limited, with problems when sources are active exactly at the same time. This procedure allows us to discover temporal periods of activity of each source. However, with a temporal analysis alone it is not possible to separate audio features of different sources precisely synchronous. The goal, now, is to learn the sources frequency behavior when only each one of them is active to predict those moments when they overlap. Applying a simple frequency association, results improve considerably with separated soundtracks of a better audible quality. In this report, we will analyze in depth all the steps of the proposed approach, remarking the motivation of each one of them

A multimodal pattern recognition framework for speaker detection

Speaker detection is an important component of a speech-based user interface. Audiovisual speaker detection, speech and speaker recognition or speech synthesis for example find multiple applications in human-computer interaction, multimedia content indexing, biometrics, etc. Generally speaking, any interface which relies on speech for communication requires an estimate of the user's speaking state (i.e. whether or not he/she is speaking to the system) for its reliable functioning. One needs therefore to identify the speaker and discriminate from other users or background noise. A human observer would perform such a task very easily, although this decision results from a complex cognitive process referred to as decision-making. Generally speaking, this process starts with the acquisition by the human being of information about the environment, through each of its five senses. The brain then integrates these multiple information. An amazing property of this multi-sensory integration by the brain, as pointed out by cognitive sciences, is the perception of stimuli of different modalities as originating from a single source, provided they are synchronized in space and time. A speaker is a bimodal source emitting jointly an auditory signal and a visual signal (the motion of the articulators during speech production). The two signals are obviously co-occurring spatio-temporally. This interesting property allows us – as human observers – to discriminate between a speaking mouth and a mouth whose motion is not related with the auditory signal. This dissertation deals with the modelling of such a complex decision-making, using a pattern recognition procedure. A pattern recognition process comprises all the stages of an investigation, from data acquisition to classification and assessment of the results. In the audiovisual speaker detection problem, tackled more specifically in this thesis, the data are acquired using only one microphone and camera. The pattern recognizer integrates and combines these two modalities to perform and is therefore denoted as "multimodal". This multimodal approach is expected to increase the performance of the system. But it also raises many questions such as what should be fused, when in the decision process this fusion should take place, and how is it to be achieved. This thesis provides answers to each of these issues through the proposition of detailed solutions for each step of the classification process. The basic principle is to evaluate the synchrony between the audio and video features extracted from potentially speaking mouths, in order to classify each mouth as speaking or not. This synchrony is evaluated through a mutual information based function. A key to success is the extraction of suitable features. The audiovisual data are then processed through an information theoretic feature extraction framework after having been acquired and represented in a tractable way. This feature extraction framework uses jointly the two modalities in a feature-level fusion scheme. This way, the information originating from the common source is recovered while the independent noise is discarded. This approach is shown to minimize the probability of committing an error on the source estimate. These optimal features are put as inputs of the classifier, defined through a hypothesis testing approach. Using jointly the two modalities, it outputs a single decision about the class label of each candidate mouth region ("speaker" or "non-speaker"). Therefore, the acoustic and visual information are combined at both the feature and the decision levels, so that we can talk about a hybrid fusion method. The hypothesis testing approach gives means for evaluating the performance of the classifier itself but also of the whole pattern recognition system. In particular, the added-value offered by the feature extraction step can be assessed. The framework is applied in a first time with a particular emphasis on the audio modality: the information theoretic feature extraction addresses the optimization of the audio features using jointly the video information. As a result, audio features specific to speech production are produced. The system evaluation framework establishes that putting these features at input of the classifier increases its discrimination power with respect to equivalent non-optimized features. Then the enhancement of the video content is addressed more specifically. The mouth motion is obviously the suitable video representation for handling a task such as speaker detection. However, only an estimate of this motion, the optical flow, can be obtained. This estimation relies on the intensity gradient of the image sequence. Graph theory is used to establish a probabilistic model of the relationships between the audio, the motion and the image intensity gradient, in the particular case of a speaking mouth. The interpretation of this model leads back to the optimization function defined for the information theoretic feature extraction. As a result, a scale-space approach is proposed for estimating the optical flow, where the strength of the smoothness constraint is controlled via a mutual information based criterion involving both the audio and the video information. First results are promising even if more extensive tests should be carried out, in noisy conditions in particular. As a conclusion, this thesis proposes a complete pattern recognition framework dedicated to audiovisual speaker detection and minimizing the probability of misclassifying a mouth as "speaker" or "non-speaker". The importance of fusing the audio and video content as soon as at the feature level is demonstrated through the system evaluation stage included in the pattern recognition process

Nonlinear approximation with redundant multi-component dictionaries

The problem of efficiently representing and approximating digital data is an open challenge and it is of paramount importance for many applications. This dissertation focuses on the approximation of natural signals as an organized combination of mutually connected elements, preserving and at the same time benefiting from their inherent structure. This is done by decomposing a signal onto a multi-component, redundant collection of functions (dictionary), built by the union of several subdictionaries, each of which is designed to capture a specific behavior of the signal. In this way, instead of representing signals as a superposition of sinusoids or wavelets many alternatives are available. In addition, since dictionaries we are interested in are overcomplete, the decomposition is non-unique. This gives us the possibility of adaptation, choosing among many possible representations the one which best fits our purposes. On the other hand, it also requires more complex approximation techniques whose theoretical decomposition capacity and computational load have to be carefully studied. In general, we aim at representing a signal with few and meaningful components. If we are able to represent a piece of information by using only few elements, it means that such elements can capture its main characteristics, allowing to compact the energy carried by a signal into the smallest number of terms. In such a framework, this work also proposes analysis methods which deal with the goal of considering the a priori information available when decomposing a structured signal. Indeed, a natural signal is not only an array of numbers, but an expression of a physical event about which we usually have a deep knowledge. Therefore, we claim that it is worth exploiting its structure, since it can be advantageous not only in helping the analysis process, but also in making the representation of such information more accessible and meaningful. The study of an adaptive image representation inspired and gave birth to this work. We often refer to images and visual information throughout the course of the dissertation. However, the proposed approximation setting extends to many different kinds of structured data and examples are given involving videos and electrocardiogram signals. An important part of this work is constituted by practical applications: first of all we provide very interesting results for image and video compression. Then, we also face the problem of signal denoising and, finally, promising achievements in the field of source separation are presented

Sparse image approximation with application to flexible image coding

Natural images are often modeled through piecewise-smooth regions. Region edges, which correspond to the contours of the objects, become, in this model, the main information of the signal. Contours have the property of being smooth functions along the direction of the edge, and irregularities on the perpendicular direction. Modeling edges with the minimum possible number of terms is of key importance for numerous applications, such as image coding, segmentation or denoising. Standard separable basis fail to provide sparse enough representation of contours, due to the fact that this kind of basis do not see the regularity of edges. In order to be able to detect this regularity, a new method based on (possibly redundant) sets of basis functions able to capture the geometry of images is needed. This thesis presents, in a first stage, a study about the features that basis functions should have in order to provide sparse representations of a piecewise-smooth image. This study emphasizes the need for edge-adapted basis functions, capable to accurately capture local orientation and anisotropic scaling of image structures. The need of different anisotropy degrees and orientations in the basis function set leads to the use of redundant dictionaries. However, redundant dictionaries have the inconvenience of giving no unique sparse image decompositions, and from all the possible decompositions of a signal in a redundant dictionary, just the sparsest is needed. There are several algorithms that allow to find sparse decompositions over redundant dictionaries, but most of these algorithms do not always guarantee that the optimal approximation has been recovered. To cope with this problem, a mathematical study about the properties of sparse approximations is performed. From this, a test to check whether a given sparse approximation is the sparsest is provided. The second part of this thesis presents a novel image approximation scheme, based on the use of a redundant dictionary. This scheme allows to have a good approximation of an image with a number of terms much smaller than the dimension of the signal. This novel approximation scheme is based on a dictionary formed by a combination of anisotropically refined and rotated wavelet-like mother functions and Gaussians. An efficient Full Search Matching Pursuit algorithm to perform the image decomposition in such a dictionary is designed. Finally, a geometric image coding scheme based on the image approximated over the anisotropic and rotated dictionary of basis functions is designed. The coding performances of this dictionary are studied. Coefficient quantization appears to be of crucial importance in the design of a Matching Pursuit based coding scheme. Thus, a quantization scheme for the MP coefficients has been designed, based on the theoretical energy upper bound of the MP algorithm and the empirical observations of the coefficient distribution and evolution. Thanks to this quantization, our image coder provides low to medium bit-rate image approximations, while it allows for on the fly resolution switching and several other affine image transformations to be performed directly in the transformed domain

Toward sparse and geometry adapted video approximations

Video signals are sequences of natural images, where images are often modeled as piecewise-smooth signals. Hence, video can be seen as a 3D piecewise-smooth signal made of piecewise-smooth regions that move through time. Based on the piecewise-smooth model and on related theoretical work on rate-distortion performance of wavelet and oracle based coding schemes, one can better analyze the appropriate coding strategies that adaptive video codecs need to implement in order to be efficient. Efficient video representations for coding purposes require the use of adaptive signal decompositions able to capture appropriately the structure and redundancy appearing in video signals. Adaptivity needs to be such that it allows for proper modeling of signals in order to represent these with the lowest possible coding cost. Video is a very structured signal with high geometric content. This includes temporal geometry (normally represented by motion information) as well as spatial geometry. Clearly, most of past and present strategies used to represent video signals do not exploit properly its spatial geometry. Similarly to the case of images, a very interesting approach seems to be the decomposition of video using large over-complete libraries of basis functions able to represent salient geometric features of the signal. In the framework of video, these features should model 2D geometric video components as well as their temporal evolution, forming spatio-temporal 3D geometric primitives. Through this PhD dissertation, different aspects on the use of adaptivity in video representation are studied looking toward exploiting both aspects of video: its piecewise nature and the geometry. The first part of this work studies the use of localized temporal adaptivity in subband video coding. This is done considering two transformation schemes used for video coding: 3D wavelet representations and motion compensated temporal filtering. A theoretical R-D analysis as well as empirical results demonstrate how temporal adaptivity improves coding performance of moving edges in 3D transform (without motion compensation) based video coding. Adaptivity allows, at the same time, to equally exploit redundancy in non-moving video areas. The analogy between motion compensated video and 1D piecewise-smooth signals is studied as well. This motivates the introduction of local length adaptivity within frame-adaptive motion compensated lifted wavelet decompositions. This allows an optimal rate-distortion performance when video motion trajectories are shorter than the transformation "Group Of Pictures", or when efficient motion compensation can not be ensured. After studying temporal adaptivity, the second part of this thesis is dedicated to understand the fundamentals of how can temporal and spatial geometry be jointly exploited. This work builds on some previous results that considered the representation of spatial geometry in video (but not temporal, i.e, without motion). In order to obtain flexible and efficient (sparse) signal representations, using redundant dictionaries, the use of highly non-linear decomposition algorithms, like Matching Pursuit, is required. General signal representation using these techniques is still quite unexplored. For this reason, previous to the study of video representation, some aspects of non-linear decomposition algorithms and the efficient decomposition of images using Matching Pursuits and a geometric dictionary are investigated. A part of this investigation concerns the study on the influence of using a priori models within approximation non-linear algorithms. Dictionaries with a high internal coherence have some problems to obtain optimally sparse signal representations when used with Matching Pursuits. It is proved, theoretically and empirically, that inserting in this algorithm a priori models allows to improve the capacity to obtain sparse signal approximations, mainly when coherent dictionaries are used. Another point discussed in this preliminary study, on the use of Matching Pursuits, concerns the approach used in this work for the decompositions of video frames and images. The technique proposed in this thesis improves a previous work, where authors had to recur to sub-optimal Matching Pursuit strategies (using Genetic Algorithms), given the size of the functions library. In this work the use of full search strategies is made possible, at the same time that approximation efficiency is significantly improved and computational complexity is reduced. Finally, a priori based Matching Pursuit geometric decompositions are investigated for geometric video representations. Regularity constraints are taken into account to recover the temporal evolution of spatial geometric signal components. The results obtained for coding and multi-modal (audio-visual) signal analysis, clarify many unknowns and show to be promising, encouraging to prosecute research on the subject