Lucio Sansone

Web personalization based on static information and dynamic user behavior

The explosive growth of the web is at the basis of the great interest into web usage mining techniques in both commercial and research areas. In this paper, a web personalization strategy based on pattern recognition techniques is presented. This strategy takes into account both static information, by means of classical clustering algorithms, and dynamic behavior of a user, proposing a novel and effective re-classification algorithm. Experiments have been carried out in order to validate our approach and evaluate the proposed algorithm.

A web personalization system based on web usage mining techniques

In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. In this work we present a Web mining strategy for Web personalization based on a novel pattern recognition strategy which analyzes and classifies both static and dynamic features. The results of experiments on the data from a large commercial web site are presented to show the effectiveness of the proposed system.

A Formal Model for Video Shot Segmentation and its Application via Animate Vision

The first step in a video indexing process is the segmentation of videos into meaningful parts called shots. In this paper we present a formal model of the video shot segmentation process. Starting from a mathematical characterization of the most common transition effects, a video segmentation algorithm capable to detect both abrupt and gradual transitions is proposed. The proposed algorithm is based on the computation of an arbitrary similarity measure between consecutive frames of a video. The algorithm has been tested adopting a similarity metric based on the Animate Vision theory and results have been reported.

Managing Uncertainties in Image Databases: A Fuzzy Approach

In this paper we present a fuzzy approach for image databases. We exploit the concept of NF2 relational model as a foundation for building image catalogues containing the semantic description of a given image database. New algebraic operators are defined in order to capture the fuzziness related to the semantic descriptors of an image. We compare our model to the First Normal Form annotated relation model, and show that in a number of interesting cases they can be considered equivalent, from the operational point of view, but in general NF2 relational model is more powerful, and provides a more suitable framework for dealing with uncertainties in image databases.

A Semantic Topic Identification System for Document Retrieval on the Web

Effective search and retrieval are fundamental for realizing the full potential of the Web. Although nowadays search engines perform much better than few years ago, big improvements are still needed with respect to the relevance of the retrieved documents to the users query and the presentation of the results. In this paper we present the prototype of a News retrieval system which exploits Wordnets semantics in identifying the topic of retrieved documents and ranking them according to their relevance to the query. Also the system provides a short summary of each document, helping the user in browsing the result collection.

An indexing approach for representing multimedia objects in high-dimensional spaces based on expectation maximization algorithm

In this paper we introduce a new indexing approach to representing multimedia object classes generated by the Expectation Maximization clustering algorithm in a balanced and dynamic tree structure. To this aim the EM algorithm has been modified in order to obtain at each step of its recursive application balanced clusters. In this manner our tree provides a simple and practical solution to index clustered data and support efficient retrieval of the nearest neighbors in high dimensional object spaces.