Model-driven and Data-driven Approaches for some Object Recognition Problems

Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change

Spatiotemporal visual analysis of human actions

In this dissertation we propose four methods for the recognition of human activities. In all four of them, the representation of the activities is based on spatiotemporal features that are automatically detected at areas where there is a significant amount of independent motion, that is, motion that is due to ongoing activities in the scene. We propose the use of spatiotemporal salient points as features throughout this dissertation. The algorithms presented, however, can be used with any kind of features, as long as the latter are well localized and have a well-defined area of support in space and time. We introduce the utilized spatiotemporal salient points in the first method presented in this dissertation. By extending previous work on spatial saliency, we measure the variations in the information content of pixel neighborhoods both in space and time, and detect the points at the locations and scales for which this information content is locally maximized. In this way, an activity is represented as a collection of spatiotemporal salient points. We propose an iterative linear space-time warping technique in order to align the representations in space and time and propose to use Relevance Vector Machines (RVM) in order to classify each example into an action category. In the second method proposed in this dissertation we propose to enhance the acquired representations of the first method. More specifically, we propose to track each detected point in time, and create representations based on sets of trajectories, where each trajectory expresses how the information engulfed by each salient point evolves over time. In order to deal with imperfect localization of the detected points, we augment the observation model of the tracker with background information, acquired using a fully automatic background estimation algorithm. In this way, the tracker favors solutions that contain a large number of foreground pixels. In addition, we perform experiments where the tracked templates are localized on specific parts of the body, like the hands and the head, and we further augment the tracker’s observation model using a human skin color model. Finally, we use a variant of the Longest Common Subsequence algorithm (LCSS) in order to acquire a similarity measure between the resulting trajectory representations, and RVMs for classification. In the third method that we propose, we assume that neighboring salient points follow a similar motion. This is in contrast to the previous method, where each salient point was tracked independently of its neighbors. More specifically, we propose to extract a novel set of visual descriptors that are based on geometrical properties of three-dimensional piece-wise polynomials. The latter are fitted on the spatiotemporal locations of salient points that fall within local spatiotemporal neighborhoods, and are assumed to follow a similar motion. The extracted descriptors are invariant in translation and scaling in space-time. Coupling the neighborhood dimensions to the scale at which the corresponding spatiotemporal salient points are detected ensures the latter. The descriptors that are extracted across the whole dataset are subsequently clustered in order to create a codebook, which is used in order to represent the overall motion of the subjects within small temporal windows.Finally,we use boosting in order to select the most discriminative of these windows for each class, and RVMs for classification. The fourth and last method addresses the joint problem of localization and recognition of human activities depicted in unsegmented image sequences. Its main contribution is the use of an implicit representation of the spatiotemporal shape of the activity, which relies on the spatiotemporal localization of characteristic ensembles of spatiotemporal features. The latter are localized around automatically detected salient points. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. During training, we use boosting in order to create codebooks of characteristic feature ensembles for each class. Subsequently, we construct class-specific spatiotemporal models, which encode where in space and time each codeword ensemble appears in the training set. During testing, each activated codeword ensemble casts probabilistic votes concerning the spatiotemporal localization of the activity, according to the information stored during training. We use a Mean Shift Mode estimation algorithm in order to extract the most probable hypotheses from each resulting voting space. Each hypothesis corresponds to a spatiotemporal volume which potentially engulfs the activity, and is verified by performing action category classification with an RVM classifier

Parallel Tracking and Mapping for Manipulation Applications with Golem Krang

Implementing a simultaneous localization and mapping system and an image semantic segmentation method on a mobile manipulation. The application of the SLAM is working towards navigating among obstacles in unknown environments. The object detection method will be integrated for future manipulation tasks such as grasping. This work will be demonstrated on a real robotics hardware system in the lab.Outgoin

RECOGNITION OF FACES FROM SINGLE AND MULTI-VIEW VIDEOS

Face recognition has been an active research field for decades. In recent years, with videos playing an increasingly important role in our everyday life, video-based face recognition has begun to attract considerable research interest. This leads to a wide range of potential application areas, including TV/movies search and parsing, video surveillance, access control etc. Preliminary research results in this field have suggested that by exploiting the abundant spatial-temporal information contained in videos, we can greatly improve the accuracy and robustness of a visual recognition system. On the other hand, as this research area is still in its infancy, developing an end-to-end face processing pipeline that can robustly detect, track and recognize faces remains a challenging task. The goal of this dissertation is to study some of the related problems under different settings. We address the video-based face association problem, in which one attempts to extract face tracks of multiple subjects while maintaining label consistency. Traditional tracking algorithms have difficulty in handling this task, especially when challenging nuisance factors like motion blur, low resolution or significant camera motions are present. We demonstrate that contextual features, in addition to face appearance itself, play an important role in this case. We propose principled methods to combine multiple features using Conditional Random Fields and Max-Margin Markov networks to infer labels for the detected faces. Different from many existing approaches, our algorithms work in online mode and hence have a wider range of applications. We address issues such as parameter learning, inference and handling false positves/negatives that arise in the proposed approach. Finally, we evaluate our approach on several public databases. We next propose a novel video-based face recognition framework. We address the problem from two different aspects: To handle pose variations, we learn a Structural-SVM based detector which can simultaneously localize the face fiducial points and estimate the face pose. By adopting a different optimization criterion from existing algorithms, we are able to improve localization accuracy. To model other face variations, we use intra-personal/extra-personal dictionaries. The intra-personal/extra-personal modeling of human faces has been shown to work successfully in the Bayesian face recognition framework. It has additional advantages in scalability and generalization, which are of critical importance to real-world applications. Combining intra-personal/extra-personal models with dictionary learning enables us to achieve state-of-arts performance on unconstrained video data, even when the training data come from a different database. Finally, we present an approach for video-based face recognition using camera networks. The focus is on handling pose variations by applying the strength of the multi-view camera network. However, rather than taking the typical approach of modeling these variations, which eventually requires explicit knowledge about pose parameters, we rely on a pose-robust feature that eliminates the needs for pose estimation. The pose-robust feature is developed using the Spherical Harmonic (SH) representation theory. It is extracted using the surface texture map of a spherical model which approximates the subject's head. Feature vectors extracted from a video are modeled as an ensemble of instances of a probability distribution in the Reduced Kernel Hilbert Space (RKHS). The ensemble similarity measure in RKHS improves both robustness and accuracy of the recognition system. The proposed approach outperforms traditional algorithms on a multi-view video database collected using a camera network

Graph Priors, Optimal Transport, and Deep Learning in Biomedical Discovery

Recent advances in biomedical data collection allows the collection of massive datasets measuring thousands of features in thousands to millions of individual cells. This data has the potential to advance our understanding of biological mechanisms at a previously impossible resolution. However, there are few methods to understand data of this scale and type. While neural networks have made tremendous progress on supervised learning problems, there is still much work to be done in making them useful for discovery in data with more difficult to represent supervision. The flexibility and expressiveness of neural networks is sometimes a hindrance in these less supervised domains, as is the case when extracting knowledge from biomedical data. One type of prior knowledge that is more common in biological data comes in the form of geometric constraints. In this thesis, we aim to leverage this geometric knowledge to create scalable and interpretable models to understand this data. Encoding geometric priors into neural network and graph models allows us to characterize the models’ solutions as they relate to the fields of graph signal processing and optimal transport. These links allow us to understand and interpret this datatype. We divide this work into three sections. The first borrows concepts from graph signal processing to construct more interpretable and performant neural networks by constraining and structuring the architecture. The second borrows from the theory of optimal transport to perform anomaly detection and trajectory inference efficiently and with theoretical guarantees. The third examines how to compare distributions over an underlying manifold, which can be used to understand how different perturbations or conditions relate. For this we design an efficient approximation of optimal transport based on diffusion over a joint cell graph. Together, these works utilize our prior understanding of the data geometry to create more useful models of the data. We apply these methods to molecular graphs, images, single-cell sequencing, and health record data

Learning Discriminative Feature Representations for Visual Categorization

Learning discriminative feature representations has attracted a great deal of attention due to its potential value and wide usage in a variety of areas, such as image/video recognition and retrieval, human activities analysis, intelligent surveillance and human-computer interaction. In this thesis we first introduce a new boosted key-frame selection scheme for action recognition. Specifically, we propose to select a subset of key poses for the representation of each action via AdaBoost and a new classifier, namely WLNBNN, is then developed for final classification. The experimental results of the proposed method are 0.6% - 13.2% better than previous work. After that, a domain-adaptive learning approach based on multiobjective genetic programming (MOGP) has been developed for image classification. In this method, a set of primitive 2-D operators are randomly combined to construct feature descriptors through the MOGP evolving and then evaluated by two objective fitness criteria, i.e., the classification error and the tree complexity. Later, the (near-)optimal feature descriptor can be obtained. The proposed approach can achieve 0.9% ∼ 25.9% better performance compared with state-of-the-art methods. Moreover, effective dimensionality reduction algorithms have also been widely used for obtaining better representations. In this thesis, we have proposed a novel linear unsupervised algorithm, termed Discriminative Partition Sparsity Analysis (DPSA), explicitly considering different probabilistic distributions that exist over the data points, simultaneously preserving the natural locality relationship among the data. All these above methods have been systematically evaluated on several public datasets, showing their accurate and robust performance (0.44% - 6.69% better than the previous) for action and image categorization. Targeting efficient image classification , we also introduce a novel unsupervised framework termed evolutionary compact embedding (ECE) which can automatically learn the task-specific binary hash codes. It is regarded as an optimization algorithm which combines the genetic programming (GP) and a boosting trick. The experimental results manifest ECE significantly outperform others by 1.58% - 2.19% for classification tasks. In addition, a supervised framework, bilinear local feature hashing (BLFH), has also been proposed to learn highly discriminative binary codes on the local descriptors for large-scale image similarity search. We address it as a nonconvex optimization problem to seek orthogonal projection matrices for hashing, which can successfully preserve the pairwise similarity between different local features and simultaneously take image-to-class (I2C) distances into consideration. BLFH produces outstanding results (0.017% - 0.149% better) compared to the state-of-the-art hashing techniques

Towards multi-modal face recognition in the wild

Face recognition aims at utilizing the facial appearance for the identification or verification of human individuals, and has been one of the fundamental research areas in computer vision. Over the past a few decades, face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics and so on. Many existing face recognition methods are evaluated with faces collected in labs, and does not generalize well in reality. Compared with faces captured in labs, faces in the wild are inherently multi-modal distributed. The multi-modality issue leads to significant intra-class variations, and usually requires a large amount of labeled samples to cover the wide range of modalities. These difficulties make unconstrained face recognition even more challenging, and pose a considerable gap between laboratorial research and industrial practice. To bridge the gap, we set focus on multi-modal face recognition in the unconstrained environment in this thesis. This thesis introduces several approaches to address the aforementioned specific challenges. Accordingly, the approaches included can be generally categorized into two research directions. The first direction explores a series of deep learning based methods in handling the large intra-class variations in multi-modal face recognition. The combination of modalities in the wild is unpredictable, and thus is difficult to explicitly define in advance. It is desirable to design a framework adaptive to the modality-driven variations in the specific scenarios. To this end, Deep Neural Network (DNN) is adopted as the basis, as DNN learns the feature representation and the classifier with reference to the specific target objective directly. To begin with, we aims to learn a part-based facial representation with deep neural networks to address face verification in the wild. In particular, the proposed framework consists of two deliberate components: a Deep Mixture Model (DMM) to find accurate patch correspondence and a Convolutional Fusion Network (CFN) to learn the fusion of multiple patch-specific facial features. This framework is specifically designed to handle local distortions caused by modalities such as pose and illumination. The next work introduces the conditional partition of the sample space into deep learning to tackle face recognition with regard to modalities in a general sense. Without any prior knowledge of modality, the proposed network learns the hidden modalities of faces, based on which the initial sample space is partitioned so that modality-specific feature representation can be learnt accordingly. The other direction is Semi-Supervised Learning with videos to tackle the deficiency of labeled training samples. In particular, a novel Semi-Supervised Learning strategy is proposed for the problem of celebrity identification by harvesting the “confident” unlabeled samples from the vast video sources. The video context information is adopted to iteratively enrich the diversity of the initial labeled set so that the performance of learnt classifier can be gradually improved. In this thesis, all these works are evaluated with extensive experiments in the corresponding sections. The connection and difference among the three approaches are further discussed in the conclusion section.Open Acces

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

기계학습 시스템 설계를 위한 방법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 최기영.Machine learning has been paid attention because intelligence such as recognition, decision making, and recommendation is a helpful utility in industrial, medical, transportation, entertainment systems, and others that human need to interact with. As machine learning techniques are extensively applied to various areas, the needs for more robust algorithms and more efficient hardware have been increased. In order to develop an efficient machine learning system, we have researched from high-level algorithm down to low-level hardware logicthe main focus of our work is on ensemble machine learning and stochastic computing (SC). The first work is to combine multiple components, i.e., multiple feature extractors (FE) and multiple classifiers in the aspect of pattern recognition. Ensemble of multiple components is one of challenging approaches for constructing a more accurate classifier. It can handle difficult problems where a single classifier easily makes a wrong decision due to lack of training or parameter optimization. Combining the decisions of participating classifiers statistically reduces the risk of wrong decision. We suggest a hierarchical ensemble framework of multiple feature extractors and multiple classifiers (MFMC). The second work is to construct efficient hardware building blocks for machine learning in order to reduce system complexity and generate high area- and energy-efficient logic, where we exploit the property of machine learning systems that does not require accurate computations. We select stochastic computing (SC), which is an alternative paradigm to conventional binary arithmetic computing. SC can boost efficiency in terms of area, power, and error tolerance, while relaxing the accuracy of computation. The third work is to combine both machine learning and stochastic computing, where we select deep learning. This work presents an efficient DNN design with stochastic computing. Observing that directly adopting stochastic computing to DNN has some challenges including random error fluctuation, range limitation, and overhead in accumulation, we address these problems by removing near-zero weights, applying weight-scaling, and integrating the activation function with the accumulator. The approach allows an easy implementation of early decision termination with a fixed hardware design by exploiting the progressive precision characteristics of stochastic computing, which was not easy with existing approaches. Experimental results show that our approach outperforms the conventional binary logic in terms of gate area, latency, and power consumption.1. Introduction 1 1.1 Hierarchical Ensemble Learning Framework 1 1.2 Hardware Building Block for Machine Learning By Using Stochastic Computing 1 1.2.1 Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks 5 2. A Design Framework for Hierarchical Ensemble of Multiple Feature Extractors and Multiple Classifiers 7 2.1 Introduction 7 2.2 Related work 9 2.3 Proposed hierarchical ensemble system 12 2.3.1 Local Mapping Block and Global Mapping Block 12 2.3.2 Complexity comparison according to composition of LMB 15 2.3.3 Motivation for differentiating local and global mappings17 2.3.4 Reinforcement learning for LMB 19 2.3.5 Construction of Bayesian network from GMB 24 2.4 Experimental results 32 2.4.1 Measure of effectiveness for WMV and RL 33 2.4.2 Pedestrian detection dataset 35 2.4.3 Comparison between GMB and AdaBoost 41 2.4.4 UCI Multiple Features dataset 42 2.4.5 LMB selection 44 2.4.6 Discussion 45 2.5 Conclusion 46 3. Synthesis of Efficient Stochastic Logic for Many-Variable Expressions 49 3.1 Introduction 49 3.2 Related Work 52 3.3 SC Logic Synthesis for Multivariate Expressions 54 3.3.1 Probabilistic Logic 55 3.3.2 Definitions 58 3.3.3 Overview of the Proposed Method 60 3.3.4 Direct Synthesis VS. Kernel-based Synthesis 60 3.3.5 SC Kernel 63 3.3.6 Prime SC Kernel 65 3.3.7 iSC Kernel 68 3.3.8 Relationship Between iSC Kernels 70 3.3.9 Hybrid Scheme 75 3.3.10 Cost Function 76 3.3.11 SC Synthesis Algorithm 78 3.4 Experimental Results 82 3.4.1 Performance of SC Logic Synthesis Algorithm 83 3.4.2 Quality of Synthesis Results 84 3.4.3 Comparison of Accuracy 89 3.5 Conclusion 90 4. An Energy-Efficient Random Number Generator for Stochastic Circuits 91 4.1 Introduction 91 4.2 II. Background 92 4.2.1 Preliminaries 92 4.2.2 Shortcomings of Conventional Approaches 93 4.3 III. Proposed Stochastic Number Generator 96 4.3.1 Overview of the Proposed SNG 96 4.3.2 Even-distribution Encoding 96 4.3.3 Inter-group Randomization 98 4.3.4 Proposed Building Block for Bit Shuffling 100 4.3.5 Intra-group Randomization 102 4.4 Experimental Results 103 4.4.1 Accuracy of Generated Stochastic Bit Stream 104 4.4.2 Area, Delay, Power, Energy and SCC Average 104 4.4.3 Energy Efficiency When Operated under Maximal Precision 105 4.5 Conclusion 106 5. Approximate De-randomizer for Stochastic Circuits 107 5.1 Introduction 107 5.2 Proposed Approximate Parallel Counter 108 5.2.1 Analysis for Gate Count in 1-layer Approximate PC 109 5.2.2 Analysis for Error in 1-layer Approximate PC 110 5.3 Experimental Results 111 5.4 Conclusion 112 6. Dynamic Energy-Accuracy Trade-off Using Stochastic Computing in Deep Neural Networks 113 6.1 Introduction 113 6.2 Background 115 6.4 DNN Using Stochastic Circuit 117 6.4.1 Overview of the Proposed DNN using SC 117 6.4.2 Removing Near-Zero Weights 119 6.4.3 Applying Weight Scaling 120 6.4.4 Activation Function with Accumulation 121 6.5 Early Decision Termination 125 6.5.1 Moving Average Tracking Output Trends 126 6.6 Experimental Results 127 6.6.1 Accuracy of DNN Using SC 128 6.6.2 Effectiveness of Early Decision Termination 129 6.6.3 Comparison of Synthesis Results 130 6.7 Conclusion 132 7. Conclusion 134 Bibliography 136 요약(국문초록) 144Docto