Sprite Learning and Object Category Recognition using Invariant Features

Institute for Adaptive and Neural ComputationThis thesis explores the use of invariant features for learning sprites from image sequences, and for recognising object categories in images. A popular framework for the interpretation of image sequences is the layers or sprite model of e.g.Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretised transformations (e.g. translations, or affines) for each layer. We show that by using invariant features (e.g. Lowe’s SIFT features) and clustering their motions we can reduce or eliminate the search and thus learn the sprites much faster. The algorithm is demonstrated on example image sequences. We introduce the Generative Template of Features (GTF), a parts-based model for visual object category detection. The GTF consists of a number of parts, and for each part there is a corresponding spatial location distribution and a distribution over ‘visual words’ (clusters of invariant features). We evaluate the performance of the GTF model for object localisation as compared to other techniques, and show that such a relatively simple model can give state-of- the-art performance. We also discuss the connection of the GTF to Hough-transform-like methods for object localisation

Uncertainty-driven Forest Predictors for Vertebra Localization and Segmentation

Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT

Semantic Localization and Mapping in Robot Vision

Integration of human semantics plays an increasing role in robotics tasks such as mapping, localization and detection. Increased use of semantics serves multiple purposes, including giving computers the ability to process and present data containing human meaningful concepts, allowing computers to employ human reasoning to accomplish tasks. This dissertation presents three solutions which incorporate semantics onto visual data in order to address these problems. First, on the problem of constructing topological maps from sequence of images. The proposed solution includes a novel image similarity score which uses dynamic programming to match images using both appearance and relative positions of local features simultaneously. An MRF is constructed to model the probability of loop-closures and a locally optimal labeling is found using Loopy-BP. The recovered loop closures are then used to generate a topological map. Results are presented on four urban sequences and one indoor sequence. The second system uses video and annotated maps to solve localization. Data association is achieved through detection of object classes, annotated in prior maps, rather than through detection of visual features. To avoid the caveats of object recognition, a new representation of query images is introduced consisting of a vector of detection scores for each object class. Using soft object detections, hypotheses about pose are refined through particle filtering. Experiments include both small office spaces, and a large open urban rail station with semantically ambiguous places. This approach showcases a representation that is both robust and can exploit the plethora of existing prior maps for GPS-denied environments while avoiding the data association problems encountered when matching point clouds or visual features. Finally, a purely vision-based approach for constructing semantic maps given camera pose and simple object exemplar images. Object response heatmaps are combined with known pose to back-project detection information onto the world. These update the world model, integrating information over time as the camera moves. The approach avoids making hard decisions on object recognition, and aggregates evidence about objects in the world coordinate system. These solutions simultaneously showcase the contribution of semantics in robotics and provide state of the art solutions to these fundamental problems

Modèles structurés pour la reconnaissance d'actions dans des vidéos réalistes

Cette thèse décrit de nouveaux modèles pour la reconnaissance de catégories d'actions comme "ouvrir une porte" ou "courir" dans des vidéos réalistes telles que les films. Nous nous intéressons tout particulièrement aux propriétés structurelles des actions : comment les décomposer, quelle en est la structure caractéristique et comment utiliser cette information afin de représenter le contenu d'une vidéo. La difficulté principale à laquelle nos modèles s'attellent réside dans la satisfaction simultanée de deux contraintes antagonistes. D'une part, nous devons précisément modéliser les aspects discriminants d'une action afin de pouvoir clairement identifier les différences entre catégories. D'autre part, nos représentations doivent être robustes en conditions réelles, c'est-à-dire dans des vidéos réalistes avec de nombreuses variations visuelles en termes d'acteurs, d'environnements et de points de vue. Dans cette optique, nous proposons donc trois modèles précis et robustes à la fois, qui capturent les relations entre parties d'actions ainsi que leur contenu. Notre approche se base sur des caractéristiques locales - notamment les points d'intérêts spatio-temporels et le flot optique - et a pour objectif d'organiser l'ensemble des descripteurs locaux décrivant une vidéo. Nous proposons aussi des noyaux permettant de comparer efficacement les représentations structurées que nous introduisons. Bien que nos modèles se basent tous sur les principes mentionnés ci-dessus, ils différent de par le type de problème traité et la structure sur laquelle ils reposent. Premièrement, nous proposons de modéliser une action par une séquence de parties temporelles atomiques correspondant à une décomposition sémantique. De plus, nous décrivons comment apprendre un modèle flexible de la structure temporelle dans le but de localiser des actions dans des vidéos de longue durée. Deuxièmement, nous étendons nos idées à l'estimation et à la représentation de la structure spatio-temporelle d'activités plus complexes. Nous décrivons un algorithme d'apprentissage non supervisé permettant de dégager automatiquement une décomposition hiérarchique du contenu dynamique d'une vidéo. Nous utilisons la structure arborescente qui en résulte pour modéliser une action de manière hiérarchique. Troisièmement, au lieu de comparer des modèles structurés, nous explorons une autre alternative : directement comparer des modèles de structure. Pour cela, nous représentons des actions de courte durée comme des séries temporelles en haute dimension et étudions comment la dynamique temporelle d'une action peut être utilisée pour améliorer les performances des modèles non structurés formant l'état de l'art en reconnaissance d'actions. Dans ce but, nous proposons un noyau calculant de manière efficace la similarité entre les dépendances temporelles respectives de deux actions. Nos trois approches et leurs assertions sont à chaque fois validées par des expériences poussées sur des bases de données publiques parmi les plus difficiles en reconnaissance d'actions. Nos résultats sont significativement meilleurs que ceux de l'état de l'art, illustrant ainsi à quel point la structure des actions est importante afin de bâtir des modèles précis et robustes pour la reconnaissance d'actions dans des vidéos réalistes.This dissertation introduces novel models to recognize broad action categories - like "opening a door" and "running" - in real-world video data such as movies and internet videos. In particular, we investigate how an action can be decomposed, what is its discriminative structure, and how to use this information to accurately represent video content. The main challenge we address lies in how to build models of actions that are simultaneously information-rich - in order to correctly differentiate between different action categories - and robust to the large variations in actors, actions, and videos present in real-world data. We design three robust models capturing both the content of and the relations between action parts. Our approach consists in structuring collections of robust local features - such as spatio-temporal interest points and short-term point trajectories. We also propose efficient kernels to compare our structured action representations. Even if they share the same principles, our methods differ in terms of the type of problem they address and the structure information they rely on. We, first, propose to model a simple action as a sequence of meaningful atomic temporal parts. We show how to learn a flexible model of the temporal structure and how to use it for the problem of action localization in long unsegmented videos. Extending our ideas to the spatio-temporal structure of more complex activities, we, then, describe a large-scale unsupervised learning algorithm used to hierarchically decompose the motion content of videos. We leverage the resulting tree-structured decompositions to build hierarchical action models and provide an action kernel between unordered binary trees of arbitrary sizes. Instead of structuring action models, we, finally, explore another route: directly comparing models of the structure. We view short-duration actions as high-dimensional time-series and investigate how an action's temporal dynamics can complement the state-of-the-art unstructured models for action classification. We propose an efficient kernel to compare the temporal dependencies between two actions and show that it provides useful complementary information to the traditional bag-of-features approach. In all three cases, we conducted thorough experiments on some of the most challenging benchmarks used by the action recognition community. We show that each of our methods significantly outperforms the related state of the art, thus highlighting the importance of structure information for accurate and robust action recognition in real-world videos.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Classifying tracked objects in far-field video surveillance

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 67-70).Automated visual perception of the real world by computers requires classification of observed physical objects into semantically meaningful categories (such as 'car' or 'person'). We propose a partially-supervised learning framework for classification of moving objects-mostly vehicles and pedestrians-that are detected and tracked in a variety of far-field video sequences, captured by a static, uncalibrated camera. We introduce the use of scene-specific context features (such as image-position of objects) to improve classification performance in any given scene. At the same time, we design a scene-invariant object classifier, along with an algorithm to adapt this classifier to a new scene. Scene-specific context information is extracted through passive observation of unlabelled data. Experimental results are demonstrated in the context of outdoor visual surveillance of a wide variety of scenes.by Biswajit Bose.S.M

An Unsupervised Approach to Modelling Visual Data

For very large visual datasets, producing expert ground-truth data for training supervised algorithms can represent a substantial human effort. In these situations there is scope for the use of unsupervised approaches that can model collections of images and automatically summarise their content. The primary motivation for this thesis comes from the problem of labelling large visual datasets of the seafloor obtained by an Autonomous Underwater Vehicle (AUV) for ecological analysis. It is expensive to label this data, as taxonomical experts for the specific region are required, whereas automatically generated summaries can be used to focus the efforts of experts, and inform decisions on additional sampling. The contributions in this thesis arise from modelling this visual data in entirely unsupervised ways to obtain comprehensive visual summaries. Firstly, popular unsupervised image feature learning approaches are adapted to work with large datasets and unsupervised clustering algorithms. Next, using Bayesian models the performance of rudimentary scene clustering is boosted by sharing clusters between multiple related datasets, such as regular photo albums or AUV surveys. These Bayesian scene clustering models are extended to simultaneously cluster sub-image segments to form unsupervised notions of “objects” within scenes. The frequency distribution of these objects within scenes is used as the scene descriptor for simultaneous scene clustering. Finally, this simultaneous clustering model is extended to make use of whole image descriptors, which encode rudimentary spatial information, as well as object frequency distributions to describe scenes. This is achieved by unifying the previously presented Bayesian clustering models, and in so doing rectifies some of their weaknesses and limitations. Hence, the final contribution of this thesis is a practical unsupervised algorithm for modelling images from the super-pixel to album levels, and is applicable to large datasets