Com o vislumbramento de aplicações que exigiam representações em espaços multidimensionais, surgiu a necessidade de desenvolvimento de métodos de acessos eficientes a estes dados representados em R^d. Dentre as aplicações precursoras dos métodos de acessos multidimensionais, podemos citar os sistemas de geoprocessamento, aplicativos 3D e simuladores. Posteriormente, os métodos de acessos multidimensionais também apresentaram-se como uma importante ferramenta no projeto de classificadores, principalmente classificadores pelos vizinhos mais próximos. Com isso, expandiu-se o espaço de representação, que antes se limitava no máximo a quatro dimensões, para dimensionalidades superiores a mil. Dentre os vários métodos de acesso multidimensional existentes, destaca-se uma classe de métodos baseados em árvores balanceadas com representação em R^d. Estes métodos constituem evoluções da árvore de acesso unidimenisonal B-tree e herdam várias características deste último. Neste trabalho, apresentamos alguns métodos de acessos dessa classe de forma a ilustrar a idéia central destes algoritmos e propomos e implementamos um novo método de acesso, a PCA-tree. A PCA-tree utiliza uma heurística de quebra de nós baseada na extração da componente principal das amostras a serem divididas. Um hiperplano que possui essa componente principal como seu vetor normal é definido como o elemento que divide o espaço associado ao nó. A partir dessa idéia básica geramos uma estrutura de dados e algoritmos que utilizam gerenciamento de memória secundária como a B-tree. Finalmente...
Os Sistemas de Gerenciamento de Banco de Dados (SGBDs) existentes são muito sofisticados, eficientes e rápidos na recuperação de informações envolvendo dados de tipos tradicionais, tais como números, texto, etc., mas existem muitas limitações em se tratando de recuperar informações quando os tipos de dados são mais complexos, isto é, dados multi-dimensionais. Considerando os problemas existentes com a indexação e recuperação de dados multi-dimensionais, este trabalho propõe um sistema híbrido que combina um modelo de Redes Neurais da família ART, ART2-A, com uma estrutura de dados, Slim-Tree, que é um método de acesso a dados no espaço métrico. Esta proposta é uma alternativa para realizar o processo de agrupamento de dados de forma "inteligente" tal que os dados pertencentes aos agrupamentos (clusters) possam ser recuperados a partir da Slim-Tree correspondente. O sistema híbrido proposto é capaz de realizar consultas do tipo: busca por abrangência e dos k-vizinhos mais próximos, o que não é característica comum das redes neurais artificiais. Além disto, os experimentos realizados mostram que o desempenho do sistema foi igual ou superior ao desempenho obtido pela Slim-Tree.; Database Management System (DBMS) are very sophisticated...
We have developed a web server (ProteinDBS) for the life science community to search for similar protein tertiary structures in real time. This system applies computer visualization techniques to extract the predominant visual patterns encoded in two-dimensional distance matrices generated from the three-dimensional coordinates of protein chains. When meaningful contents, represented in a multi-dimensional feature space, have been extracted from distance matrices, an advanced indexing structure, Entropy Balanced Statistical (EBS) k-d tree, is utilized to index the data. Our system is able to return search results in ranked order from a database with 46 075 chains in seconds, exhibiting a reasonably high degree of precision. To our knowledge, this is the first real-time search engine for protein structure comparison. ProteinDBS provides two types of query method: query by Protein Data Bank protein chain ID and by new structures uploaded by users. The system is hosted at http://ProteinDBS.rnet.missouri.edu.
The advent of smart TVs has reshaped the TV-consumer interaction by combining TVs with mobile-like applications and access to the Internet. However, consumers are still unable to seamlessly interact with the contents being streamed. An example of such limitation is TV shopping, in which a consumer makes a purchase of a product or item displayed in the current TV show. Currently, consumers can only stop the current show and attempt to find a similar item in the Web or an actual store. It would be more convenient if the consumer could interact with the TV to purchase interesting items.
Towards the realization of TV shopping, this dissertation proposes a scalable multimedia content processing framework. Two main challenges in TV shopping are addressed: the efficient detection of products in the content stream, and the retrieval of similar products given a consumer-selected product. The proposed framework consists of three components. The first component performs computational and temporal aware multimedia abstraction to select a reduced number of frames that summarize the important information in the video stream. By both reducing the number of frames and taking into account the computational cost of the subsequent detection phase, this component component allows the efficient detection of products in the stream. The second component realizes the detection phase. It executes scalable product detection using multi-cue optimization. Additional information cues are formulated into an optimization problem that allows the detection of complex products...
The advent of smart TVs has reshaped the TV-consumer interaction by combining TVs with mobile-like applications and access to the Internet. However, consumers are still unable to seamlessly interact with the contents being streamed. An example of such limitation is TV shopping, in which a consumer makes a purchase of a product or item displayed in the current TV show. Currently, consumers can only stop the current show and attempt to find a similar item in the Web or an actual store. It would be more convenient if the consumer could interact with the TV to purchase interesting items. ^ Towards the realization of TV shopping, this dissertation proposes a scalable multimedia content processing framework. Two main challenges in TV shopping are addressed: the efficient detection of products in the content stream, and the retrieval of similar products given a consumer-selected product. The proposed framework consists of three components. The first component performs computational and temporal aware multimedia abstraction to select a reduced number of frames that summarize the important information in the video stream. By both reducing the number of frames and taking into account the computational cost of the subsequent detection phase...
Machine learning techniques play essential roles in many computer vision applications. This thesis is dedicated to two types of machine learning techniques which are important to computer vision: structured learning and binary code learning. Structured learning is for predicting complex structured output of which the components are inter-dependent. Structured outputs are common in real-world applications. The image segmentation mask is an example of structured output. Binary code learning is to learn hash functions that map data points into binary codes. The binary code representation is popular for large-scale similarity search, indexing and storage. This thesis has made practical and theoretical contributions to these two types of learning techniques. The first part of this thesis focuses on boosting based structured output prediction. Boosting is a type of methods for learning a single accurate predictor by linearly combining a set of less accurate weak learners. As a special case of structured learning, we first propose an efficient boosting method for multi-class classification, which can be applied to image classification. Different from many existing multi-class boosting methods, we train class specified weak learners by separately learning weak learners for each class. We also develop a fast coordinate descent method for solving the optimization problem...
This paper presents a general technique for optimally transforming any
dynamic data structure that operates on atomic and indivisible keys by
constant-time comparisons, into a data structure that handles unbounded-length
keys whose comparison cost is not a constant. Examples of these keys are
strings, multi-dimensional points, multiple-precision numbers, multi-key data
(e.g.~records), XML paths, URL addresses, etc. The technique is more general
than what has been done in previous work as no particular exploitation of the
underlying structure of is required. The only requirement is that the insertion
of a key must identify its predecessor or its successor.
Using the proposed technique, online suffix tree can be constructed in worst
case time $O(\log n)$ per input symbol (as opposed to amortized $O(\log n)$
time per symbol, achieved by previously known algorithms). To our knowledge,
our algorithm is the first that achieves $O(\log n)$ worst case time per input
symbol. Searching for a pattern of length $m$ in the resulting suffix tree
takes $O(\min(m\log |\Sigma|, m + \log n) + tocc)$ time, where $tocc$ is the
number of occurrences of the pattern. The paper also describes more
applications and show how to obtain alternative methods for dealing with suffix
Observed protein structures usually represent energetically favorable conformations. While not all observed structures are necessarily functional, it is generally agreed that protein structure is closely related to protein function. Given a collection of proteins sharing a common global structure, variations in their local structures at specific, critical locations may result in different biological functions. Structural relationships among proteins are important in the study of the evolution of proteins as well as in drug design and development.
Analysis of geometrical 3D protein structure has been shown to be effective with respect to classifying proteins. Prior work has shown that the Double Centroid Reduced Representation (DCRR) model is a useful geometric representation for protein structure with respect to visual models, reducing the quantity of modeled information for each amino acid, yet retaining the most important geometrical and chemical features of each: the centroids of the backbone and of the side-chain. DCRR has not yet been applied in the calculation of geometric structural similarity.
Meanwhile, multi-dimensional indexing (MDI) of protein structure combines protein structural analysis with distance metrics to facilitate structural similarity queries and is also used for clustering protein structures into related groups. In this respect...