Parametric kernels for structured data analysis

textStructured representation of input physical patterns as a set of local features has been useful for a veriety of robotics and human computer interaction (HCI) applications. It enables a stable understanding of the variable inputs. However, this representation does not fit the conventional machine learning algorithms and distance metrics because they assume vector inputs. To learn from input patterns with variable structure is thus challenging. To address this problem, I propose a general and systematic method to design distance metrics between structured inputs that can be used in conventional learning algorithms. Based on the observation of the stability in the geometric distributions of local features over the physical patterns across similar inputs, this is done combining the local similarities and the conformity of the geometric relationship between local features. The produced distance metrics, called “parametric kernels”, are positive semi-definite and require almost linear time to compute. To demonstrate the general applicability and the efficacy of this approach, I designed and applied parametric kernels to handwritten character recognition, on-line face recognition, and object detection from laser range finder sensor data. Parametric kernels achieve recognition rates competitive to state-of-the-art approaches in these tasks.Computer Science

Feature Extraction Methods for Character Recognition

Not Include

Handwritten Document Image Retrieval

Ph.DDOCTOR OF PHILOSOPH

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS

A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined

Alignment of handwritten music scores

There are musicologists that spend their time in analyzing musical pieces of more than a century ago in order to link them to another pre-existing pieces from the same author but written by different hands. It is a tedious task, since there are many representations done of a single piece through the time, and the writing variability among those representations can be extensive. The purpose would be in having a varied database of these old compositions for the study, reproduction and difusion. This work is divided into two phases. The first one, constitent in the detection of primitive present elements in each of the measures of a score using the existing transcription of the piece, thus obtaining the desired guided alignment. The second one will seek to analyze this alignment. Obtained results are encouraging.Hi ha musicòlegs que dediquen el seu temps a analitzar obres musicals de fa més d'un segle per enllaçar-les amb altres ja existents del mateix autor però escrites per mans diferents. És una tasca tediosa, doncs són moltes les representacions que s'han pogut fer d'una mateixa obra al llarg del temps, i la variabilitat d'escriptura entre aquestes pot ser molt ample. La finalitat doncs, seria la de tenir una base de dades variada d'aquestes composicions antigues per a l'estudi, reproducció i difusió. Aquest treball es divideix en dues fases. La primera, consistent en la detecció dels elements presents en cada un dels compassos d'una partitura a partir de la transcripció existent de la partitura, conseguint així un alineament guiat. La segona tractarà d'analitzar aquest alineament. Els resultats obtinguts són encoratjadors.Hay musicólogos que dedican su tiempo a analizar obras musicales de hace más de un siglo para enlazarlas con otras ya existentes del mismo autor pero escritas por manos distintas. Es una tarea tediosa, pues son muchas las representaciones que se han podido hacer de una misma obra a lo largo del tiempo, y la variabilidad de escritura entre estas puede ser muy amplia. La finalidad, pues, sería la de tener una base de datos variada de estas composiciones antiguas para el estudio, la reproducción y difusión. Este trabajo se divide en dos fases. La primera, consistente en la detección de los elementos presentes en cada uno de los compases de una partitura a partir de la transcripción existente de la partitura, consiguiendo así un alineamiento guiado. La segunda tratará de analizar este alineamiento. Los resultados obtenidos son alentadores

Methods for Structural Pattern Recognition: Complexity and Applications

Katedra kybernetik