GIACINTO , Giorgio

Despite the efforts to reduce the semantic gap between user perception of similarity and feature-based representation of images, user interaction is essential to improve retrieval performances in content based image retrieval. To this end a number of relevance feedback mechanisms are currently adopted to refine image queries. They are aimed...

In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier. Systems are effective only if the classifiers forming them...

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In pattern recognition systems, Chow's rule is commonly used to reach a trade-off between error and reject probabilities. In this paper, we investigate the effects of estimate errors affecting the a posteriori probabilities on the optimality of Chow's rule. We show that the optimal error-reject tradeoff is not provided by Chow's rule if the a...

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors....

An approach to classifier combination based on the concept of 'dynamic classifier selection' is presented. The results reported show that the proposed approach enables effective image classification systems to be developed.

Browse Conference Publications > Image Analysis and Processing ... Comparison and combination of adaptive query shifting and feature relevance learning for content-based image retrieval This paper appears in: Image Analysis and Processing, 2001. Proceedings. 11th International Conference on Date of Conference: 26-28 Sep 2001 Author(s):...

In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At present, the common "operation" mechanism of MCSs is the "combination" of classifiers outputs. Recently, some researchers pointed out the potentialities of "dynamic...

Nowadays machine-learning algorithms are increasingly being applied in security-related applications like spam and malware detection, aiming to detect never-before-seen attacks and novel threats. However, such techniques may expose specific vulnerabilities that may be exploited by carefully-crafted attacks. Support Vector Machines (SVMs) are a...

Pattern recognition systems have been widely used in adversarial classification tasks like spam filtering and intrusion detection in computer networks. In these applications a malicious adversary may successfully mislead a classifier by "poisoning" its training data with carefully designed attacks. Bagging is a well-known ensemble construction...

Multiple classifier systems (MCSs) based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming...

At present, the usual operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection" as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative...

Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and the feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval systems. To this end, relevance feedback mechanisms are adopted to refine...

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors....

Relevance feedback mechanisms are adopted to refine image-based queries by asking users to mark the set of retrieved images as being relevant or not. In this paper, a relevance feedback technique based on the "dissimilarity representation" of images is proposed. Each image is represented by a vector whose components are the similarity values...

In the field of pattern recognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them...

Recently, the concept of Multiple Classifier Systems was proposed as a new approach to the development of high performance image classification systems. Multiple Classifier Systems can be used to improve classification accuracy by combining the outputs of classifiers making uncorrelated errors. Unfortunately, in real image recognition problems,...

Effective retrieval of images from databases can be attained by adopting relevance feedback mechanisms. The vast majority of such mechanisms that have been proposed so far are based on modifying either the query point, or the feature space, or the similarity measure, so that the average similarity between pairs of relevant images is as minimum...

In the field of pattern recognition, the concept of multiple classifier systems (MCS) was proposed as a method for the development of high-performance classification systems. At present, the common "operation" mechanism of MCS is the "combination" of classifier outputs. Recently, some researchers have pointed out the potentialities of "dynamic...

At present, the usual operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection' as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative...

Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval systems. To this end, relevance feedback mechanisms are adopted to refine image-based...

Despite the efforts to reduce the so-called semantic gap between the user's perception of image similarity and feature-based representation of images, the interaction with the user remains fundamental to improve performances of content-based image retrieval systems. To this end, relevance feedback mechanisms are adopted to refine image-based...

Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Machines (SVMs) have been successfully exploited to tackle this problem, using one-vs-one or one-vs-all learning schemes to enable multiclass classification, and kernels designed for image classification to handle nonlinearities. To classify an...

High-retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. The user marks all the retrieved images as being either relevant or not, then the search engine exploits this relevance feedback to adapt the search to better meet user's needs. The main difficulties in exploiting relevance...