The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other regionbased retrieval systems, an image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories, such as textured-nontextured, graphphotograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. Compared with retrieval based on individual regions, the overall similarity approach 1) reduces the adverse effect of inaccurate segmentation, 2) helps to clarify the semantics of a particular region, and 3) enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to several databases, including a database of about 200,000 general-purpose images, has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.

Users often can not easily express their queries. For example, in a multimedia/image by content setting, the user might want photographs with sunsets; in current systems, like QBIC, the user has to give a sample query, and to specify the relative importance of color, shape and texture. Even worse, the user might want correlations between attributes, like, for example, in a traditional, medical record database, a medical researcher might want to find "mildly overweight patients", where the implied query would be "weight/height ≈ 4 lb/inch". Our goal is to provide a user-friendly, but theoretically solid method, to handle such queries. We allow the user to give several examples, and, optionally, their 'goodness' scores, and we propose a novel method to "guess" which attributes are important, which correlations are important, and with what weight. Our contributions are twofold: (a) we formalize the problem as a minimization problem and show how to solve for the optimal solution, completely av...

Categorization of text images into content-oriented classes would be a useful capability in a variety of document handling systems. Many methods can be usedtocategorize texts once their words are known, but OCR can garble a large proportion of words, particularly when low quality images are used. Despite this, we show for one data set that fax quality images can be categorized with nearly the same accuracy as the original text. Further, the categorization system can be trained on noisy OCR output, without need for the true text of any image, or for editing of OCR output. The useofavector space classi er and training method robust to large feature sets, combined with discarding of low frequency OCR output strings are the key to our approach. 1

Intelligent Information Retrieval is concerned with the application of intelligent techniques, like for example semantic networks, neural networks and inference nets to Information Retrieval. The eld of research has seen a number of applications of Constrained Spreading Activation (CSA) techniques on domain knowledge networks. However, there has never been any application of these techniques to the World Wide Web. The Web is a very important information resource, but users nd that looking for a relevant piece of information in the Web can be like "looking for a needle in a haystack". We were therefore motivated to design and develop a prototype system, WebSCSA (Web Search by CSA), that applies a CSA technique to retrieve information from the Web using an ostensive approach to querying similar to query-by-example. In this paper we describe the system and its underlying model. Furthermore, we report on an experiment carried out with human subjects to evaluate the e ectiveness of WebSCSA. We tested whether WebSCSA improves retrieval of relevant information on top of Web search engines results and how well WebSCSA serves as an agent browser for the user. The results of the experiments are promising, and show that there is much potential for further research on the use of CSA techniques to search the Web.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii I Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 A. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. The #arti#cial life bridge" . . . . . . . . . . . . . . . . . . . . . . 1 2. From nature to technology . . . . . . . . . . . . . . . . . . . . . 3 3. From technology to nature . . . . . . . . . . . . . . . . . . . . . 4 B. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 II Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 A. Background: Machine learning . . . . . . . . . . . . . . . . . . . . . 10 1. Evolutionary algorithms . . . . . . . . . . . . . . . . . . . . . . . 11 2. Endogenous #tness . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3. Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . 18 B. Local selection . . . . . . . . . . . . . . . . . . . . . . . . . . ...

In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalised probabilistic model of Information Retrieval. The model is based on the concept of "uncertainty sampling" a technique that allows for relevance feedback both on relevant and non relevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile. Previously at Dipartimento di Elettronica e Informatica, Universit'a di Padova, Padova, Italy. 1 Introduction New information services deal with a variety of processes concerning the acquisition and the delivery of information. With the increasing availability of information in electronic form, it becomes more important and feasible to have automatic methods to filter information. Users may receive large amounts of information electronic, like for example electronic mail or news, and systems for Information Filtering (IF) are required to select from a large amount o...

In this paper we give anoverview of Agent Technology (AT), and describe an agent developed for KPN, that has as its core functionality a Data-Mining & Knowledge Discovery system. It is able to browse autonomously the WWW and collect pages according to a pro le extracted from pages that are assessed relevant by the user. An explorative experiment showed marked improvement over a traditional search engine. 1