Pasquale Savino

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We propose a new approach to perform approximate similarity search in metric spaces. The idea at the basis of this technique is that when two objects are very close one to each other they ’see’ the world around them in the same way. Accordingly, we can use a measure of dissimilarity between the view of the world, from the perspective of the two objects, in(More)
Motivated by the urgent need to improve the efficiency of similarity queries, approximate similarity retrieval is investigated in the environment of a metric tree index called the M-tree. Three different approximation techniques are proposed, which show how to forsake query precision for improved performance. Measures are defined that can quantify the(More)
In order to speedup retrieval in large collections of data, index structures partition the data into subsets so that query requests can be evaluated without examining the entire collection. As the complexity of modern data types grows, metric spaces have become a popular paradigm for similarity retrieval. We propose a new index structure, called D-Index,(More)
Similarity search structures for metric data typically bound object partitions by ball regions. Since regions can overlap, a relevant issue is to estimate the proximity of regions in order to predict the number of objects in the regions' intersection. This paper analyzes the problem using a probabilistic approach and provides a solution that effectively(More)
Signature files provide an efficient access method for text in documents, but retrieval is usually limited to finding documents that contain a specified Boolean pattern of words. Effective retrieval requires that documents with similar meanings be found through a process of plausible inference. The simplest way of implementing this retrieval process is to(More)
We propose a new efficient and accurate technique for generic approximate similarity searching, based on the use of inverted files. We represent each object of a dataset by the ordering of a number of reference objects according to their distance from the object itself. In order to compare two objects in the dataset, we compare the two corresponding(More)
This paper describes the MILOS Multimedia Content Management System: a general purpose software component tailored to support design and effective implementation of digital library applications. MILOS supports the storage and content based retrieval of any multimedia documents whose descriptions are provided by using arbitrary metadata models represented in(More)
In this paper, a technique for making more effective the similarity search process of images in a Multimedia Content Management System is proposed. The contentbased retrieval process integrates the search on different multimedia components, linked in XML structures. Depending on the specific characteristics of an image data set, some features can be more(More)