Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based image/ video deblocking framework via properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. The proposed method first decomposes an image/video frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a ¡§blocking component¡¨ and a ¡§non-blocking component¡¨ by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original image/video details. Experimental results demonstrate the efficacy of the proposed algorithm.

We study in this thesis a particular machine learning approach to represent signals that that consists of modelling data as linear combinations of a few elements from a learned dictionary. It can be viewed as an extension of the classical wavelet framework, whose goal is to design such dictionaries (often orthonormal basis) that are adapted to natural signals. An important success of dictionary learning methods has been their ability to model natural image patches and the performance of image denoising algorithms that it has yielded. We address several open questions related to this framework: How to efficiently optimize the dictionary? How can the model be enriched by adding a structure to the dictionary? Can current image processing tools based on this method be further improved? How should one learn the dictionary when it is used for a different task than signal reconstruction? How can it be used for solving computer vision problems? We answer these questions with a multidisciplinarity approach, using tools from statistical machine learning, convex and stochastic optimization, image and signal processing, computer vision, but also optimization on graphs.

<p>It has been increasingly important to develop statistical models to manage large-scale high-dimensional image data. This thesis presents novel hierarchical nonparametric Bayesian models for joint analysis of imagery and text. This thesis consists two main parts.</p><p>The first part is based on single image processing. We first present a spatially dependent model for simultaneous image segmentation and interpretation. Given a corrupted image, by imposing spatial inter-relationships within imagery, the model not only improves reconstruction performance but also yields smooth segmentation. Then we develop online variational Bayesian algorithm for dictionary learning to process large-scale datasets, based on online stochastic optimization with a natu- ral gradient step. We show that dictionary is learned simultaneously with image reconstruction on large natural images containing tens of millions of pixels.</p><p>The second part applies dictionary learning for joint analysis of multiple image and text to infer relationship among images. We show that feature extraction and image organization with annotation (when available) can be integrated by unifying dictionary learning and hierarchical topic modeling. We present image organization in both "flat" and hierarchical constructions. Compared with traditional algorithms feature extraction is separated from model learning, our algorithms not only better fits the datasets, but also provides richer and more interpretable structures of image</p> / Dissertation

The representation of a signal using a learned dictionary instead of predefined operators, such as wavelets, has led to state-of-the-art results in various applications such as denoising, texture analysis, and face recognition. The area of dictionary learning is closely associated with sparse representation, which means that the signal is represented using few atoms in the dictionary. Despite recent advances in the computation of a dictionary using fast algorithms such as K-SVD, online learning, and cyclic coordinate descent, which make the computation of a dictionary from millions of data samples computationally feasible, the dictionary is mainly computed using unsupervised approaches such as k-means. These approaches learn the dictionary by minimizing the reconstruction error without taking into account the category information, which is not optimal in classification tasks. In this thesis, we propose a supervised dictionary learning (SDL) approach by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and their corresponding labels is maximized. To maximize this dependency, the recently-introduced Hilbert Schmidt independence criterion (HSIC) is used. The learned dictionary is compact and has closed form; the proposed approach is fast. We show that it outperforms other unsupervised and supervised dictionary learning approaches in the literature on real-world data. Moreover, the proposed SDL approach has as its main advantage that it can be easily kernelized, particularly by incorporating a data-driven kernel such as a compression-based kernel, into the formulation. In this thesis, we propose a novel compression-based (dis)similarity measure. The proposed measure utilizes a 2D MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes on textures. Experimental results show that by incorporating the proposed measure as a kernel into our SDL, it significantly improves the performance of a supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures, as well as state-of-the-art SDL methods. It also improves the computation speed by about 40% compared to its closest rival. Eventually, we have extended the proposed SDL to multiview learning, where more than one representation is available on a dataset. We propose two different multiview approaches: one fusing the feature sets in the original space and then learning the dictionary and sparse coefficients on the fused set; and the other by learning one dictionary and the corresponding coefficients in each view separately, and then fusing the representations in the space of the dictionaries learned. We will show that the proposed multiview approaches benefit from the complementary information in multiple views, and investigate the relative performance of these approaches in the application of emotion recognition.

With widespread availability of low-cost devices capable of photo shooting and high-volume video recording, we are facing explosion of both image and video data. The sheer volume of such visual data poses both challenges and opportunities in machine learning and computer vision research. In image classification, most of previous research has focused on small to mediumscale data sets, containing objects from dozens of categories. However, we could easily access images spreading thousands of categories. Unfortunately, despite the well-known advantages and recent advancements of multi-class classification techniques in machine learning, complexity concerns have driven most research on such super large-scale data set back to simple methods such as nearest neighbor search, one-vs-one or one-vs-rest approach. However, facing image classification problem with such huge task space, it is no surprise that these classical algorithms, often favored for their simplicity, will be brought to their knees not only because of the training time and storage cost they incur, but also because of the conceptual awkwardness of such algorithms in massive multi-class paradigms. Therefore, it is our goal to directly address the bigness of image data, not only the large number of training images and high-dimensional image features, but also the large task space. Specifically, we present algorithms capable of efficiently and effectively training classifiers that could differentiate tens of thousands of image classes. Similar to images, one of the major difficulties in video analysis is also the huge amount of data, in the sense that videos could be hours long or even endless. However, it is often true that only a small portion of video contains important information. Consequently, algorithms that could automatically detect unusual events within streaming or archival video would significantly improve the efficiency of video analysis and save valuable human attention for only the most salient contents. Moreover, given lengthy recorded videos, such as those captured by digital cameras on mobile phones, or surveillance cameras, most users do not have the time or energy to edit the video such that only the most salient and interesting part of the original video is kept. To this end, we also develop algorithm for automatic video summarization, without human intervention. Finally, we further extend our research on video summarization into a supervised formulation, where users are asked to generate summaries for a subset of a class of videos of similar nature. Given such manually generated summaries, our algorithm learns the preferred storyline within the given class of videos, and automatically generates summaries for the rest of videos in the class, capturing the similar storyline as in those manually summarized videos.

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New methods for monitoring the condition of roller element bearings in rotating machinery offer possibilities to reduce repair- and maintenance costs, and reduced use of environmentally harmful lubricants. One such method is sparse representation of vibration signals using matching pursuit with dictionary learning, which so far has been tested on PCs with data from controlled tests. Further testing requires a platform capable of signal processing and control in more realistic experiments. This thesis focuses on the integration of a hybrid CPU-FPGA hardware system with a 16-bit analog-to-digital converter and an oil pump, granting the possibility of collecting real-time data, executing the algorithm in closed loop and supplying lubrication to the machine under test, if need be. The aforementioned algorithm is implemented in a Zynq-7000 System-on-Chip and the analog-to-digital converter as well as the pump motor controller are integrated. This platform enables portable operation of the matching pursuit with dictionary learning in the field under a larger variety of environmental and operational conditions, conditions which might prove difficult to reproduce in a laboratory setup. The platform developed throughout this project can collect data using the analog-to-digital converter and operations can be performed on that data in both the CPU and the FPGA. A test of the system function at a sampling rate of 5 kHz is presented and the input and output are verified to function correctly.

Segmentação é uma etapa essencial para sistemas de pré-triagem de lesões melanocíticas. Neste trabalho, um novo método para segmentar lesões melanocíticas em imagens de câmera padrão (i.e., imagens macroscópicas) é apresentado. Inicialmente, para reduzir artefatos indesejáveis, os efeitos de sombra são atenuados na imagem macroscópica e uma présegmentação é obtida usando um esquema que combina a transformada wavelet com a transformada watershed. Em seguida, uma imagem de variação textural projetada para melhorar a discriminabilidade da lesão em relação ao fundo é obtida e a região présegmentada é usada para o aprendizado de um dicionário inicial e de uma representação inicial via um método de fatoração de matrizes não-negativas. Uma versão nãosupervisionada e não-paramétrica do método de aprendizado de dicionário baseado em teoria da informação é proposta para otimizar esta representação, selecionando o subconjunto de átomos que maximiza a compactividade e a representatividade do dicionário aprendido. Por fim, a imagem da lesão de pele é representada usando o dicionário aprendido e segmentada com o método de corte normalizado em grafos. Nossos resultados experimentais baseados em uma base de imagens bastante utilizada sugerem que o método proposto tende a fornecer melhores resultados do que os métodos estado-da-arte analisados (em termos do erro XOR). / Segmentation is an essential step for the automated pre-screening of melanocytic lesions. In this work, a new method for segmenting melanocytic lesions in standard camera images (i.e., macroscopic images) is presented. Initially, to reduce unwanted artifacts, shading effects are attenuated in the macroscopic image and a pre-segmentation is obtained using a scheme that combines the wavelet transform and the watershed transform. Afterwards, a textural variation image designed to enhance the skin lesion against the background is obtained, and the presegmented skin lesion region is used to learn an initial dictionary and an initial representation via a nonnegative matrix factorization method. An unsupervised and non-parametric version of the information-theoretic dictionary learning method is proposed to optimize this representation by selecting the subset of atoms that maximizes the learned dictionary compactness and representation. Finally, the skin lesion image is represented using the learned dictionary and segmented with the normalized graph cuts method. Our experimental results based on a widely used image dataset suggest that the proposed method tends to provide more accurate skin lesion segmentations than comparable state-of-the-art methods (in terms of the XOR error).

abstract: Large-scale $\ell_1$-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. In many applications, it remains challenging to apply the sparse learning model to large-scale problems that have massive data samples with high-dimensional features. One popular and promising strategy is to scaling up the optimization problem in parallel. Parallel solvers run multiple cores on a shared memory system or a distributed environment to speed up the computation, while the practical usage is limited by the huge dimension in the feature space and synchronization problems. In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer's Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

Segmentação é uma etapa essencial para sistemas de pré-triagem de lesões melanocíticas. Neste trabalho, um novo método para segmentar lesões melanocíticas em imagens de câmera padrão (i.e., imagens macroscópicas) é apresentado. Inicialmente, para reduzir artefatos indesejáveis, os efeitos de sombra são atenuados na imagem macroscópica e uma présegmentação é obtida usando um esquema que combina a transformada wavelet com a transformada watershed. Em seguida, uma imagem de variação textural projetada para melhorar a discriminabilidade da lesão em relação ao fundo é obtida e a região présegmentada é usada para o aprendizado de um dicionário inicial e de uma representação inicial via um método de fatoração de matrizes não-negativas. Uma versão nãosupervisionada e não-paramétrica do método de aprendizado de dicionário baseado em teoria da informação é proposta para otimizar esta representação, selecionando o subconjunto de átomos que maximiza a compactividade e a representatividade do dicionário aprendido. Por fim, a imagem da lesão de pele é representada usando o dicionário aprendido e segmentada com o método de corte normalizado em grafos. Nossos resultados experimentais baseados em uma base de imagens bastante utilizada sugerem que o método proposto tende a fornecer melhores resultados do que os métodos estado-da-arte analisados (em termos do erro XOR). / Segmentation is an essential step for the automated pre-screening of melanocytic lesions. In this work, a new method for segmenting melanocytic lesions in standard camera images (i.e., macroscopic images) is presented. Initially, to reduce unwanted artifacts, shading effects are attenuated in the macroscopic image and a pre-segmentation is obtained using a scheme that combines the wavelet transform and the watershed transform. Afterwards, a textural variation image designed to enhance the skin lesion against the background is obtained, and the presegmented skin lesion region is used to learn an initial dictionary and an initial representation via a nonnegative matrix factorization method. An unsupervised and non-parametric version of the information-theoretic dictionary learning method is proposed to optimize this representation by selecting the subset of atoms that maximizes the learned dictionary compactness and representation. Finally, the skin lesion image is represented using the learned dictionary and segmented with the normalized graph cuts method. Our experimental results based on a widely used image dataset suggest that the proposed method tends to provide more accurate skin lesion segmentations than comparable state-of-the-art methods (in terms of the XOR error).