Giving eyes to ICT!, or How does a computer recognize a cow?

Het door Schouten en andere onderzoekers op het CWI ontwikkelde systeem berust op het beschrijven van beelden met behulp van fractale meetkunde. De menselijke waarneming blijkt mede daardoor zo efficiënt omdat zij sterk werkt met gelijkenissen. Het ligt dus voor de hand het te zoeken in wiskundige methoden die dat ook doen. Schouten heeft daarom beeldcodering met behulp van 'fractals' onderzocht. Fractals zijn zelfgelijkende meetkundige figuren, opgebouwd door herhaalde transformatie (iteratie) van een eenvoudig basispatroon, dat zich daardoor op steeds kleinere schalen vertakt. Op elk niveau van detaillering lijkt een fractal op zichzelf (Droste-effect). Met fractals kan men vrij eenvoudig bedrieglijk echte natuurvoorstellingen maken. Fractale beeldcodering gaat ervan uit dat het omgekeerde ook geldt: een beeld effectief opslaan in de vorm van de basispatronen van een klein aantal fractals, samen met het voorschrift hoe het oorspronkelijke beeld daaruit te reconstrueren. Het op het CWI in samenwerking met onderzoekers uit Leuven ontwikkelde systeem is mede gebaseerd op deze methode. ISBN 906196502

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

Introspective knowledge acquisition for case retrieval networks in textual case base reasoning.

Textual Case Based Reasoning (TCBR) aims at effective reuse of information contained in unstructured documents. The key advantage of TCBR over traditional Information Retrieval systems is its ability to incorporate domain-specific knowledge to facilitate case comparison beyond simple keyword matching. However, substantial human intervention is needed to acquire and transform this knowledge into a form suitable for a TCBR system. In this research, we present automated approaches that exploit statistical properties of document collections to alleviate this knowledge acquisition bottleneck. We focus on two important knowledge containers: relevance knowledge, which shows relatedness of features to cases, and similarity knowledge, which captures the relatedness of features to each other. The terminology is derived from the Case Retrieval Network (CRN) retrieval architecture in TCBR, which is used as the underlying formalism in this thesis applied to text classification. Latent Semantic Indexing (LSI) generated concepts are a useful resource for relevance knowledge acquisition for CRNs. This thesis introduces a supervised LSI technique called sprinkling that exploits class knowledge to bias LSI's concept generation. An extension of this idea, called Adaptive Sprinkling has been proposed to handle inter-class relationships in complex domains like hierarchical (e.g. Yahoo directory) and ordinal (e.g. product ranking) classification tasks. Experimental evaluation results show the superiority of CRNs created with sprinkling and AS, not only over LSI on its own, but also over state-of-the-art classifiers like Support Vector Machines (SVM). Current statistical approaches based on feature co-occurrences can be utilized to mine similarity knowledge for CRNs. However, related words often do not co-occur in the same document, though they co-occur with similar words. We introduce an algorithm to efficiently mine such indirect associations, called higher order associations. Empirical results show that CRNs created with the acquired similarity knowledge outperform both LSI and SVM. Incorporating acquired knowledge into the CRN transforms it into a densely connected network. While improving retrieval effectiveness, this has the unintended effect of slowing down retrieval. We propose a novel retrieval formalism called the Fast Case Retrieval Network (FCRN) which eliminates redundant run-time computations to improve retrieval speed. Experimental results show FCRN's ability to scale up over high dimensional textual casebases. Finally, we investigate novel ways of visualizing and estimating complexity of textual casebases that can help explain performance differences across casebases. Visualization provides a qualitative insight into the casebase, while complexity is a quantitative measure that characterizes classification or retrieval hardness intrinsic to a dataset. We study correlations of experimental results from the proposed approaches against complexity measures over diverse casebases

Finding What You Need, and Knowing What You Can Find: Digital Tools for Palaeographers in Musicology and Beyond

This chapter examines three projects that provide musicologists with a range of resources for managing and exploring their materials: DIAMM (Digital Image Archive of Medieval Music), CMME (Computerized Mensural Music Editing) and the software Gamera. Since 1998, DIAMM has been enhancing research of scholars worldwide by providing them with the best possible quality of digital images. In some cases these images are now the only access that scholars are permitted, since the original documents are lost or considered too fragile for further handling. For many sources, however, simply creating a very high-resolution image is not enough: sources are often damaged by age, misuse (usually Medieval ‘vandalism’), or poor conservation. To deal with damaged materials the project has developed methods of digital restoration using mainstream commercial software, which has revealed lost data in a wide variety of sources. The project also uses light sources ranging from ultraviolet to infrared in order to obtain better readings of erasures or material lost by heat or water damage. The ethics of digital restoration are discussed, as well as the concerns of the document holders. CMME and a database of musical sources and editions, provides scholars with a tool for making fluid editions and diplomatic transcriptions: without the need for a single fixed visual form on a printed page, a computerized edition system can utilize one editor’s transcription to create any number of visual forms and variant versions. Gamera, a toolkit for building document image recognition systems created by Ichiro Fujinaga is a broad recognition engine that grew out of music recognition, which can be adapted and developed to perform a number of tasks on both music and non-musical materials. Its application to several projects is discussed

Visual access to lifelog data in a virtual environment

Continuous image capture via a wearable camera is currently one of the most popular methods to establish a comprehensive record of the entirety of an indi- vidual’s life experience, referred to in the research community as a lifelog. These vast image corpora are further enriched by content analysis and combined with additional data such as biometrics to generate as extensive a record of a person’s life as possible. However, interfacing with such datasets remains an active area of research, and despite the advent of new technology and a plethora of com- peting mediums for processing digital information, there has been little focus on newly emerging platforms such as virtual reality. We hypothesise that the increase in immersion, accessible spatial dimensions, and more, could provide significant benefits in the lifelogging domain over more conventional media. In this work, we motivate virtual reality as a viable method of lifelog exploration by performing an in-depth analysis using a novel application prototype built for the HTC Vive. This research also includes the development of a governing design framework for lifelog applications which supported the development of our prototype but is also intended to support the development of future such lifelog systems

Image sense disambiguation : a multimodal approach

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-136).If a picture is worth a thousand words, can a thousand words be worth a training image? Most successful object recognition algorithms require manually annotated images of objects to be collected for training. The amount of human effort required to collect training data has limited most approaches to the several hundred object categories available in the labeled datasets. While human-annotated image data is scarce, additional sources of information can be used as weak labels, reducing the need for human supervision. In this thesis, we use three types of information to learn models of object categories: speech, text and dictionaries. We demonstrate that our use of non-traditional information sources facilitates automatic acquisition of visual object models for arbitrary words without requiring any labeled image examples. Spoken object references occur in many scenarios: interaction with an assistant robot, voice-tagging of photos, etc. Existing reference resolution methods are unimodal, relying either only on image features, or only on speech recognition. We propose a method that uses both the image of the object and the speech segment referring to it to disambiguate the underlying object label. We show that even noisy speech input helps visual recognition, and vice versa. We also explore two sources of linguistic sense information: the words surrounding images on web pages, and dictionary entries for nouns that refer to objects. Keywords that index images on the web have been used as weak object labels, but these tend to produce noisy datasets with many unrelated images. We use unlabeled text, dictionary definitions, and semantic relations between concepts to learn a refined model of image sense. Our model can work with as little supervision as a single English word. We apply this model to a dataset of web images indexed by polysemous keywords, and show that it improves both retrieval of specific senses, and the resulting object classifiers.by Kate Saenko.Ph.D