Processing techniques development, volume 3. Part 2: Data preprocessing and information extraction techniques

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mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Aprendizado ativo baseado em atributos contextuais de superpixel para classificação de imagem de sensoriamento remoto

Orientadores: Alexandre Xavier Falcão, Jefersson Alex dos SantosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Recentemente, técnicas de aprendizado de máquina têm sido propostas para criar mapas temáticos a partir de imagens de sensoriamento remoto. Estas técnicas podem ser divididas em métodos de classificação baseados em pixels ou regiões. Este trabalho concentra-se na segunda abordagem, uma vez que estamos interessados em imagens com milhões de pixels e a segmentação da imagem em regiões (superpixels) pode reduzir consideravelmente o número de amostras a serem classificadas. Porém, mesmo utilizando superpixels, o número de amostras ainda é grande para anotá-las manualmente e treinar o classificador. As técnicas de aprendizado ativo propostas resolvem este problema começando pela seleção de um conjunto pequeno de amostras selecionadas aleatoriamente. Tais amostras são anotadas manualmente e utilizadas para treinar a primeira instância do classificador. Em cada iteração do ciclo de aprendizagem, o classificador atribui rótulos e seleciona as amostras mais informativas para a correção/confirmação pelo usuário, aumentando o tamanho do conjunto de treinamento. A instância do classificador é melhorada no final de cada iteração pelo seu treinamento e utilizada na iteração seguinte até que o usuário esteja satisfeito com o classificador. Observamos que a maior parte dos métodos reclassificam o conjunto inteiro de dados em cada iteração do ciclo de aprendizagem, tornando este processo inviável para interação com o usuário. Portanto, enderaçamos dois problemas importantes em classificação baseada em regiões de imagens de sensoriamento remoto: (a) a descrição efetiva de superpixels e (b) a redução do tempo requerido para seleção de amostras em aprendizado ativo. Primeiro, propusemos um descritor contextual de superpixels baseado na técnica de sacola de palavras, que melhora o resultado de descritores de cor e textura amplamente utilizados. Posteriormente, propusemos um método supervisionado de redução do conjunto de dados que é baseado em um método do estado da arte em aprendizado ativo chamado Multi-Class Level Uncertainty (MCLU). Nosso método mostrou-se tão eficaz quanto o MCLU e ao mesmo tempo consideravelmente mais eficiente. Adicionalmente, melhoramos seu desempenho por meio da aplicação de um processo de relaxação no mapa de classificação, utilizando Campos Aleatórios de MarkovAbstract: In recent years, machine learning techniques have been proposed to create classification maps from remote sensing images. These techniques can be divided into pixel- and region-based image classification methods. This work concentrates on the second approach, since we are interested in images with millions of pixels and the segmentation of the image into regions (superpixels) can considerably reduce the number of samples for classification. However, even using superpixels the number of samples is still large for manual annotation of samples to train the classifier. Active learning techniques have been proposed to address the problem by starting from a small set of randomly selected samples, which are manually labeled and used to train a first instance of the classifier. At each learning iteration, the classifier assigns labels and selects the most informative samples for user correction/confirmation, increasing the size of the training set. An improved instance of the classifier is created by training, after each iteration, and used in the next iteration until the user is satisfied with the classifier. We observed that most methods reclassify the entire pool of unlabeled samples at every learning iteration, making the process unfeasible for user interaction. Therefore, we address two important problems in region-based classification of remote sensing images: (a) the effective superpixel description and (b) the reduction of the time required for sample selection in active learning. First, we propose a contextual superpixel descriptor, based on bag of visual words, that outperforms widely used color and texture descriptors. Second, we propose a supervised method for dataset reduction that is based on a state-of-art active learning technique, called Multi-Class Level Uncertainty (MCLU). Our method has shown to be as effective as MCLU, while being considerably more efficient. Additionally, we further improve its performance by applying a relaxation process on the classification map by using Markov Random FieldsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Weakly-Supervised Alignment of Video With Text

Suppose that we are given a set of videos, along with natural language descriptions in the form of multiple sentences (e.g., manual annotations, movie scripts, sport summaries etc.), and that these sentences appear in the same temporal order as their visual counterparts. We propose in this paper a method for aligning the two modalities, i.e., automatically providing a time stamp for every sentence. Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities. We formulate this problem as an integer quadratic program, and solve its continuous convex relaxation using an efficient conditional gradient algorithm. Several rounding procedures are proposed to construct the final integer solution. After demonstrating significant improvements over the state of the art on the related task of aligning video with symbolic labels [7], we evaluate our method on a challenging dataset of videos with associated textual descriptions [36], using both bag-of-words and continuous representations for text.Comment: ICCV 2015 - IEEE International Conference on Computer Vision, Dec 2015, Santiago, Chil

Efficient Contextual Measures for Classification of Multispectral Image Data

The most common method for labeling multispectral image data classifies each pixel entirely on the basis of its own spectral signature. Such a method neither utilizes contextual information in the image nor does it incorporate secondary information related to the scene. This exclusion is generally due to the poor cost/performance efficiency of most contextual algorithms and a lack of knowledge concerning how to relate variables from different sources. In this research, several efficient spatial context measures are developed from different structural models for four-nearest-neighbor neighborhoods. Most of these measures rely on simple manipulations of label probabilities generated by a noncontextual classifier. They are efficient computationally and are effective in improving classification accuracy over the noncontextual result. Among other schemata, the measures include: average label probabilities in a neighborhood; label probabilities; combined as a function of a metric in the label probability space; and context through semantic constraints within a Bayesian framework. In addition, an efficient implementation of a contextual classifier based on compound decision theory is developed through a simplification of the structure of the contextual prior probability^ No accuracy is lost through the simplification, but computational speed is increased 15-fold. Finally, a procedure to combine label probabilities from independent data sources is proposed. A mechanism for combining the label probabilities from each of the sources as a function of their independent classification accuracies is created and evaluated

Part-Of-Speech Tagging Of Urdu in Limited Resources Scenario

We address the problem of Part-of-Speech (POS) tagging of Urdu. POS tagging is the process of assigning a part-of-speech or lexical class marker to each word in the given text. Tagging for natural languages is similar to tokenization and lexical analysis for computer languages, except that we encounter ambiguities which are to be resolved. It plays a fundamental role in various Natural Language Processing (NLP) applications such as word sense disambiguation, parsing, name entity recognition and chunking. POS tagging, particularly plays very important role in processing free-word-order languages because such languages have relatively complex morphological structure. Urdu is a morphologically rich language. Forms of the verb, as well as case, gender, and number are expressed by the morphology. It shares its morphology, phonology and grammatical structures with Hindi. It shares its vocabulary with Arabic, Persian, Sanskrit, Turkish and Pashto languages. Urdu is written using the Perso-Arabic script. POS tagging of Urdu is a necessary component for most NLP applications of Urdu. Development of an Urdu POS tagger will influence several pipelined modules of natural language understanding system, including machine translation; partial parsing and word sense disambiguation. Our objective is to develop a robust POS tagger for Urdu. We have worked on the automatic annotation of part-of-speech for Urdu. We have defined a tag-set for Urdu. We manually annotated a corpus of 10,000 sentences. We have used different machine learning methods, namely Hidden Markov Model (HMM), Maximum Entropy Model (ME) and Conditional Random Field (CRF). Further, to deal with a small-annotated corpus, we explored the use of semi-supervised learning by using an additional un-annotated corpus. We also explored the use of a dictionary to provide to us all possible POS labeling for a given word. Since Urdu is morphologically productive. Hence we augmented Hidden Markov Model, Maximum Entropy Model and Conditional Random Field with morphological features, word suffixes and POS categories of words to develop robust POS tagger for Urdu in the limited resources scenario

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201