大規模な説明文つき画像を用いたキーフレーズ推定に基づく画像説明文の自動生成

学位の種別:課程博士University of Tokyo(東京大学

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert

Large Scale Pattern Detection in Videos and Images from the Wild

PhDPattern detection is a well-studied area of computer vision, but still current methods are unstable in images of poor quality. This thesis describes improvements over contemporary methods in the fast detection of unseen patterns in a large corpus of videos that vary tremendously in colour and texture definition, captured “in the wild” by mobile devices and surveillance cameras. We focus on three key areas of this broad subject; First, we identify consistency weaknesses in existing techniques of processing an image and it’s horizontally reflected (mirror) image. This is important in police investigations where subjects change their appearance to try to avoid recognition, and we propose that invariance to horizontal reflection should be more widely considered in image description and recognition tasks too. We observe online Deep Learning system behaviours in this respect, and provide a comprehensive assessment of 10 popular low level feature detectors. Second, we develop simple and fast algorithms that combine to provide memory- and processing-efficient feature matching. These involve static scene elimination in the presence of noise and on-screen time indicators, a blur-sensitive feature detection that finds a greater number of corresponding features in images of varying sharpness, and a combinatorial texture and colour feature matching algorithm that matches features when either attribute may be poorly defined. A comprehensive evaluation is given, showing some improvements over existing feature correspondence methods. Finally, we study random decision forests for pattern detection. A new method of indexing patterns in video sequences is devised and evaluated. We automatically label positive and negative image training data, reducing a task of unsupervised learning to one of supervised learning, and devise a node split function that is invariant to mirror reflection and rotation through 90 degree angles. A high dimensional vote accumulator encodes the hypothesis support, yielding implicit back-projection for pattern detection.European Union’s Seventh Framework Programme, specific topic “framework and tools for (semi-) automated exploitation of massive amounts of digital data for forensic purposes”, under grant agreement number 607480 (LASIE IP project)

Deep invariant feature learning for remote sensing scene classification

Image classification, as the core task in the computer vision field, has proceeded at a break­neck pace. It largely attributes to the recent growth of deep learning techniques which have blown the conventional statistical methods on a plethora of benchmarks and even can outperform humans in specific image classification tasks. Despite deep learning exceeding alternative techniques, they have many apparent disadvantages that prevent them from being deployed for the general-purpose. Specifically, deep learning always requires a considerable amount of well-annotated data to circumvent the problems of over-fitting and the lacking of prior knowledge. However, manually labelled data is expensive to acquire and is impossible to incorporate the variations as much as the real world. Consequently, deep learning models usually fail when they confront with the underrepresented variations in the training data. This is the main reason why the deep learning model is barely satisfactory in the challeng­ing image recognition task that contains nuisance variations such as, Remote Sensing Scene Classification (RSSC). The classification of remote sensing scene image is a procedure of assigning the seman­tic meaning labels for the given satellite images that contain the complicated variations, such as texture and appearances. The algorithms for effectively understanding and recognising remote sensing scene images have the potential to be employed in a broad range of applications, such as urban planning, Land Use and Land Cover (LULC) determination, natural hazards detection, vegetation mapping, environmental monitoring. This inspires us to de­sign the frameworks that can automatically predict the precise label for satellite images. In our research project, we mine and define the challenges in RSSC community compared with general scene image recognition tasks. Specifically, we summarise the problems into the following perspectives. 1) Visual-semantic ambiguity: the discrepancy between visual features and semantic concepts; 2) Variations: the intra-class diversity and inter-class similarity; 3) Clutter background; 4) The small size of the training set; 5) Unsatisfactory classification accuracy in large-scale datasets. To address the aforementioned challenges, we explore a way to dynamically expand the capabilities of incorporating the prior knowledge by transforming the input data so that we can learn the globally invariant second-order features from the transformed data for improving the performance of RSSC tasks. First, we devise a recurrent transformer network (RTN) to progressively discover the discriminative regions of input images and learn the corresponding second-order features. The model is optimised using pairwise ranking loss to achieve localising discriminative parts and learning the corresponding features in a mutu­ally reinforced way. Second, we observed that existing remote sensing image datasets lack the provision of ontological structures. Therefore, a multi-granularity canonical appearance pooling (MG-CAP) model is proposed to automatically seek the implied hierarchical structures of datasets and produced covariance features contained the multi-grained information. Third, we explore a way to improve the discriminative power of the second-order features. To accomplish this target, we present a covariance feature embedding (CFE) model to im­prove the distinctive power of covariance pooling by using suitable matrix normalisation methods and a low-norm cosine similarity loss to accurately metric the distances of high­dimensional features. Finally, we improved the performance of RSSC while using fewer model parameters. An invariant deep compressible covariance pooling (IDCCP) model is presented to boost the classification accuracy for RSSC tasks. Meanwhile, we proofed the generalisability of our IDCCP model using group theory and manifold optimisation techniques. All of the proposed frameworks allow being optimised in an end-to-end manner and are well-supported by GPU acceleration. We conduct extensive experiments on the well-known remote sensing scene image datasets to demonstrate the great promotions of our proposed methods in comparison with state-of-the-art approaches

Vision-based human action recognition using machine learning techniques

The focus of this thesis is on automatic recognition of human actions in videos. Human action recognition is defined as automatic understating of what actions occur in a video performed by a human. This is a difficult problem due to the many challenges including, but not limited to, variations in human shape and motion, occlusion, cluttered background, moving cameras, illumination conditions, and viewpoint variations. To start with, The most popular and prominent state-of-the-art techniques are reviewed, evaluated, compared, and presented. Based on the literature review, these techniques are categorized into handcrafted feature-based and deep learning-based approaches. The proposed action recognition framework is then based on these handcrafted and deep learning based techniques, which are then adopted throughout the thesis by embedding novel algorithms for action recognition, both in the handcrafted and deep learning domains. First, a new method based on handcrafted approach is presented. This method addresses one of the major challenges known as “viewpoint variations” by presenting a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process which shows its suitability for real-time applications. Second, two innovative methods are presented based on deep learning approach, to go beyond the limitations of handcrafted approach. The first method is based on transfer learning using pre-trained deep learning model as a source architecture to solve the problem of human action recognition. It is experimentally confirmed that deep Convolutional Neural Network model already trained on large-scale annotated dataset is transferable to action recognition task with limited training dataset. The comparative analysis also confirms its superior performance over handcrafted feature-based methods in terms of accuracy on same datasets. The second method is based on unsupervised deep learning-based approach. This method employs Deep Belief Networks (DBNs) with restricted Boltzmann machines for action recognition in unconstrained videos. The proposed method automatically extracts suitable feature representation without any prior knowledge using unsupervised deep learning model. The effectiveness of the proposed method is confirmed with high recognition results on a challenging UCF sports dataset. Finally, the thesis is concluded with important discussions and research directions in the area of human action recognition

Indexation sémantique des images et des vidéos par apprentissage actif

Le cadre général de cette thèse est l'indexation sémantique et la recherche d'informations, appliquée à des documents multimédias. Plus précisément, nous nous intéressons à l'indexation sémantique des concepts dans des images et vidéos par les approches d'apprentissage actif, que nous utilisons pour construire des corpus annotés. Tout au long de cette thèse, nous avons montré que les principales difficultés de cette tâche sont souvent liées, en général, à l'fossé sémantique. En outre, elles sont liées au problème de classe-déséquilibre dans les ensembles de données à grande échelle, où les concepts sont pour la plupart rares. Pour l'annotation de corpus, l'objectif principal de l'utilisation de l'apprentissage actif est d'augmenter la performance du système en utilisant que peu d'échantillons annotés que possible, ainsi minimisant les coûts de l'annotations des données (par exemple argent et temps). Dans cette thèse, nous avons contribué à plusieurs niveaux de l'indexation multimédia et nous avons proposé trois approches qui succèdent des systèmes de l'état de l'art: i) l'approche multi-apprenant (ML) qui surmonte le problème de classe-déséquilibre dans les grandes bases de données, ii) une méthode de reclassement qui améliore l'indexation vidéo, iii) nous avons évalué la normalisation en loi de puissance et de l'APC et a montré son efficacité dans l'indexation multimédia. En outre, nous avons proposé l'approche ALML qui combine le multi-apprenant avec l'apprentissage actif, et nous avons également proposé une méthode incrémentale qui accélère l'approche proposé (ALML). En outre, nous avons proposé l'approche de nettoyage actif, qui aborde la qualité des annotations. Les méthodes proposées ont été tous validées par plusieurs expériences, qui ont été menées et évaluées sur des collections à grande échelle de l'indice de benchmark internationale bien connue, appelés TRECVID. Enfin, nous avons présenté notre système d'annotation dans le monde réel basé sur l'apprentissage actif, qui a été utilisé pour mener les annotations de l'ensemble du développement de la campagne TRECVID en 2011, et nous avons présenté notre participation à la tâche d'indexation sémantique de cette campagne, dans laquelle nous nous sommes classés à la 3ème place sur 19 participants.The general framework of this thesis is semantic indexing and information retrieval, applied to multimedia documents. More specifically, we are interested in the semantic indexing of concepts in images and videos by the active learning approaches that we use to build annotated corpus. Throughout this thesis, we have shown that the main difficulties of this task are often related, in general, to the semantic-gap. Furthermore, they are related to the class-imbalance problem in large scale datasets, where concepts are mostly sparse. For corpus annotation, the main objective of using active learning is to increase the system performance by using as few labeled samples as possible, thereby minimizing the cost of labeling data (e.g. money and time). In this thesis, we have contributed in several levels of multimedia indexing and proposed three approaches that outperform state-of-the-art systems: i) the multi-learner approach (ML) that overcomes the class-imbalance problem in large-scale datasets, ii) a re-ranking method that improves the video indexing, iii) we have evaluated the power-law normalization and the PCA and showed its effectiveness in multimedia indexing. Furthermore, we have proposed the ALML approach that combines the multi-learner with active learning, and also proposed an incremental method that speeds up ALML approach. Moreover, we have proposed the active cleaning approach, which tackles the quality of annotations. The proposed methods were validated through several experiments, which were conducted and evaluated on large-scale collections of the well-known international benchmark, called TrecVid. Finally, we have presented our real-world annotation system based on active learning, which was used to lead the annotations of the development set of TrecVid 2011 campaign, and we have presented our participation at the semantic indexing task of the mentioned campaign, in which we were ranked at the 3rd place out of 19 participants.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF