Multiple instance learning for sequence data with across bag dependencies

In Multiple Instance Learning (MIL) problem for sequence data, the instances inside the bags are sequences. In some real world applications such as bioinformatics, comparing a random couple of sequences makes no sense. In fact, each instance may have structural and/or functional relations with instances of other bags. Thus, the classification task should take into account this across bag relation. In this work, we present two novel MIL approaches for sequence data classification named ABClass and ABSim. ABClass extracts motifs from related instances and use them to encode sequences. A discriminative classifier is then applied to compute a partial classification result for each set of related sequences. ABSim uses a similarity measure to discriminate the related instances and to compute a scores matrix. For both approaches, an aggregation method is applied in order to generate the final classification result. We applied both approaches to solve the problem of bacterial Ionizing Radiation Resistance prediction. The experimental results of the presented approaches are satisfactory

Generative Multiple-Instance Learning Models For Quantitative Electromyography

We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems. In MIL a learner receives training instances grouped together into bags with labels for the bags only (which might not be correct for the comprised instances). Our work was motivated by the task of facilitating the diagnosis of neuromuscular disorders using sets of motor unit potential trains (MUPTs) detected within a muscle which can be cast as a MIL problem. Our approach leads to a state-of-the-art solution to the problem of muscle classification. By introducing and analyzing generative models for MIL in a general framework and examining a variety of model structures and components, our work also serves as a methodological guide to modelling MIL tasks. We evaluate our proposed methods both on MUPT datasets and on the MUSK1 dataset, one of the most widely used benchmarks for MIL.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

SVM-Based Generalized Multiple-Instance Learning via Approximate Box Counting

The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based imageretrieval

A Comparison of Multi-instance Learning Algorithms

Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms. This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems

Human shape modelling for carried object detection and segmentation

La détection des objets transportés est un des prérequis pour développer des systèmes qui cherchent à comprendre les activités impliquant des personnes et des objets. Cette thèse présente de nouvelles méthodes pour détecter et segmenter les objets transportés dans des vidéos de surveillance. Les contributions sont divisées en trois principaux chapitres. Dans le premier chapitre, nous introduisons notre détecteur d’objets transportés, qui nous permet de détecter un type générique d’objets. Nous formulons la détection d’objets transportés comme un problème de classification de contours. Nous classifions le contour des objets mobiles en deux classes : objets transportés et personnes. Un masque de probabilités est généré pour le contour d’une personne basé sur un ensemble d’exemplaires (ECE) de personnes qui marchent ou se tiennent debout de différents points de vue. Les contours qui ne correspondent pas au masque de probabilités généré sont considérés comme des candidats pour être des objets transportés. Ensuite, une région est assignée à chaque objet transporté en utilisant la Coupe Biaisée Normalisée (BNC) avec une probabilité obtenue par une fonction pondérée de son chevauchement avec l’hypothèse du masque de contours de la personne et du premier plan segmenté. Finalement, les objets transportés sont détectés en appliquant une Suppression des Non-Maxima (NMS) qui élimine les scores trop bas pour les objets candidats. Le deuxième chapitre de contribution présente une approche pour détecter des objets transportés avec une méthode innovatrice pour extraire des caractéristiques des régions d’avant-plan basée sur leurs contours locaux et l’information des super-pixels. Initiallement, un objet bougeant dans une séquence vidéo est segmente en super-pixels sous plusieurs échelles. Ensuite, les régions ressemblant à des personnes dans l’avant-plan sont identifiées en utilisant un ensemble de caractéristiques extraites de super-pixels dans un codebook de formes locales. Ici, les régions ressemblant à des humains sont équivalentes au masque de probabilités de la première méthode (ECE). Notre deuxième détecteur d’objets transportés bénéficie du nouveau descripteur de caractéristiques pour produire une carte de probabilité plus précise. Les compléments des super-pixels correspondants aux régions ressemblant à des personnes dans l’avant-plan sont considérés comme une carte de probabilité des objets transportés. Finalement, chaque groupe de super-pixels voisins avec une haute probabilité d’objets transportés et qui ont un fort support de bordure sont fusionnés pour former un objet transporté. Finalement, dans le troisième chapitre, nous présentons une méthode pour détecter et segmenter les objets transportés. La méthode proposée adopte le nouveau descripteur basé sur les super-pixels pour iii identifier les régions ressemblant à des objets transportés en utilisant la modélisation de la forme humaine. En utilisant l’information spatio-temporelle des régions candidates, la consistance des objets transportés récurrents, vus dans le temps, est obtenue et sert à détecter les objets transportés. Enfin, les régions d’objets transportés sont raffinées en intégrant de l’information sur leur apparence et leur position à travers le temps avec une extension spatio-temporelle de GrabCut. Cette étape finale sert à segmenter avec précision les objets transportés dans les séquences vidéo. Nos méthodes sont complètement automatiques, et font des suppositions minimales sur les personnes, les objets transportés, et les les séquences vidéo. Nous évaluons les méthodes décrites en utilisant deux ensembles de données, PETS 2006 et i-Lids AVSS. Nous évaluons notre détecteur et nos méthodes de segmentation en les comparant avec l’état de l’art. L’évaluation expérimentale sur les deux ensembles de données démontre que notre détecteur d’objets transportés et nos méthodes de segmentation surpassent de façon significative les algorithmes compétiteurs.Detecting carried objects is one of the requirements for developing systems that reason about activities involving people and objects. This thesis presents novel methods to detect and segment carried objects in surveillance videos. The contributions are divided into three main chapters. In the first, we introduce our carried object detector which allows to detect a generic class of objects. We formulate carried object detection in terms of a contour classification problem. We classify moving object contours into two classes: carried object and person. A probability mask for person’s contours is generated based on an ensemble of contour exemplars (ECE) of walking/standing humans in different viewing directions. Contours that are not falling in the generated hypothesis mask are considered as candidates for carried object contours. Then, a region is assigned to each carried object candidate contour using Biased Normalized Cut (BNC) with a probability obtained by a weighted function of its overlap with the person’s contour hypothesis mask and segmented foreground. Finally, carried objects are detected by applying a Non-Maximum Suppression (NMS) method which eliminates the low score carried object candidates. The second contribution presents an approach to detect carried objects with an innovative method for extracting features from foreground regions based on their local contours and superpixel information. Initially, a moving object in a video frame is segmented into multi-scale superpixels. Then human-like regions in the foreground area are identified by matching a set of extracted features from superpixels against a codebook of local shapes. Here the definition of human like regions is equivalent to a person’s probability map in our first proposed method (ECE). Our second carried object detector benefits from the novel feature descriptor to produce a more accurate probability map. Complement of the matching probabilities of superpixels to human-like regions in the foreground are considered as a carried object probability map. At the end, each group of neighboring superpixels with a high carried object probability which has strong edge support is merged to form a carried object. Finally, in the third contribution we present a method to detect and segment carried objects. The proposed method adopts the new superpixel-based descriptor to identify carried object-like candidate regions using human shape modeling. Using spatio-temporal information of the candidate regions, consistency of recurring carried object candidates viewed over time is obtained and serves to detect carried objects. Last, the detected carried object regions are refined by integrating information of their appearances and their locations over time with a spatio-temporal extension of GrabCut. This final stage is used to accurately segment carried objects in frames. Our methods are fully automatic, and make minimal assumptions about a person, carried objects and videos. We evaluate the aforementioned methods using two available datasets PETS 2006 and i-Lids AVSS. We compare our detector and segmentation methods against a state-of-the-art detector. Experimental evaluation on the two datasets demonstrates that both our carried object detection and segmentation methods significantly outperform competing algorithms

Weakly Supervised Learning Algorithms and an Application to Electromyography

In the standard machine learning framework, training data is assumed to be fully supervised. However, collecting fully labelled data is not always easy. Due to cost, time, effort or other types of constraints, requiring the whole data to be labelled can be difficult in many applications, whereas collecting unlabelled data can be relatively easy. Therefore, paradigms that enable learning from unlabelled and/or partially labelled data have been growing recently in machine learning. The focus of this thesis is to provide algorithms that enable weakly annotating unlabelled parts of data not provided in the standard supervised setting consisting of an instance-label pair for each sample, then learning from weakly as well as strongly labelled data. More specifically, the bulk of the thesis aims at finding solutions for data that come in the form of bags or groups of instances where available information about the labels is at the bag level only. This is the form of the electromyographic (EMG) data, which represent the main application of the thesis. Electromyographic (EMG) data can be used to diagnose muscles as either normal or suffering from a neuromuscular disease. Muscles can be classified into one of three labels; normal, myopathic or neurogenic. Each muscle consists of motor units (MUs). Equivalently, an EMG signal detected from a muscle consists of motor unit potential trains (MUPTs). This data is an example of partially labelled data where instances (MUs) are grouped in bags (muscles) and labels are provided for bags but not for instances. First, we introduce and investigate a weakly supervised learning paradigm that aims at improving classification performance by using a spectral graph-theoretic approach to weakly annotate unlabelled instances before classification. The spectral graph-theoretic phase of this paradigm groups unlabelled data instances using similarity graph models. Two new similarity graph models are introduced as well in this paradigm. This paradigm improves overall bag accuracy for EMG datasets. Second, generative modelling approaches for multiple-instance learning (MIL) are presented. We introduce and analyse a variety of model structures and components of these generative models and believe it can serve as a methodological guide to other MIL tasks of similar form. This approach improves overall bag accuracy, especially for low-dimensional bags-of-instances datasets like EMG datasets. MIL generative models provide an example of models where probability distributions need to be represented compactly and efficiently, especially when number of variables of a certain model is large. Sum-product networks (SPNs) represent a relatively new class of deep probabilistic models that aims at providing a compact and tractable representation of a probability distribution. SPNs are used to model the joint distribution of instance features in the MIL generative models. An SPN whose structure is learnt by a structure learning algorithm introduced in this thesis leads to improved bag accuracy for higher-dimensional datasets

Kernel learning approaches for image classification

This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the eld of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficcient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.In dieser Dissertation verwenden wir Kernmethoden für spezielle Probleme der Bildklassifikation. Kernmethoden generalisieren die Verwendung von inneren Produkten zu Distanzen zwischen allgemeinen Objekten. Das Problem der Bildklassifikation ist es, Bilder anhand des visuellen Inhaltes zu unterscheiden. Wir beschäftigen uns mit zwei wichtigen Aspekten, die in diesem Problem auftreten: lernen mit mehrdeutiger Annotation und die Kombination von verschiedenartigen Datenquellen. Unsere Ansätze sind nicht auf die Bildklassififikation beschränkt und für einen grösseren Problemkreis verwendbar. Mehrdeutige Annotationen sind ein inhärentes Problem der Bildklassifikation. Wir diskutieren verschiedene Instanzen und schlagen eine neue Unterteilung in mehrere Szenarien vor. Danach stellen wir Kernmethoden für dieses Problem vor und entwickeln einen Algorithmus, der diese effizient löst. Mit der Methode der Kernkombination werden Kernfunktionen anhand von Daten automatisch bestimmt. Wir generalisieren diesen Ansatz indem wir den Suchraum auf kontinuierlich parametrisierte Kernklassen ausgedehnen. Diese Methode wird in zwei verschiedenen Anwendungen eingesetzt. Wir betrachten spezifische Kerne für Bilddaten und lernen diese anhand von Beispielen. Schließlich vergleichen wir verschiedene Verfahren der Merkmalskombination und zeigen signifikante Verbesserungen im Bereich der Objekterkennung gegenüber bestehenden Methoden