1,721,141 research outputs found
Moving targets - When Data Classes Depend on Subjective Judgement, or they are crafted by an Adversary to Mislead Pattern Analysis Algorithms. The case of Content-Based Multimedia Retrieval and Adversarial Classification
No full text
Intrusion Detection in Computer Networks by Multiple Classifier Systems
No full textThe security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools are currently available. Intrusion detection systems aim at detecting an intruder who eluded the "first line" protection. In the paper, a pattern recognition approach to network intrusion detection based on the multiple classifier systems paradigm is proposed. The potentialities of classifier combination for data fusion and some open issues are outlined
Design of effective neural network ensembles for image classification purposes
No full textIn 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. Therefore, the fundamental need for methods aimed to design ensembles of 'error-independent' networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles. (C) 2001 Elsevier Science B.V. All rights reserved
Synthetic pattern generation for imbalanced learning in image retrieval
No full textNowadays very large archives of digital images are easily produced thanks to the wide availability of digital cameras, that are often embedded into a number of portable devices. One of the ways of exploring an image archive is to search for similar images. Relevance feedback mechanisms can be employed to refine the search, as the most similar images according to a set of visual features may not contain the same semantic concepts according to the users’ needs. Relevance feedback allows users to label the images returned by the system as being relevant or not. Then, this labelled set is used to learn the characteristics of relevant images. As the number of images provided to users to receive feedback is usually quite small, and relevant images typically represent a tiny fraction, it turns out that the learning problem is heavily imbalanced. In order to reduce this imbalance, this paper proposes the use of techniques aimed at artificially increasing the number of examples of the relevant class. The new examples are generated as new points in the feature space so that they are in agreement with the local distribution of the available relevant examples. The locality of the proposed approach makes it quite suited to relevance feedback techniques based on the Nearest-Neighbor (NN) paradigm. The effectiveness of the proposed approach is assessed on two image datasets and comparisons with editing techniques that eliminate redundancies in non-relevant examples are also reported
Information fusion in content based image retrieval: A comprehensive overview
Full text linkAn ever increasing part of communication between persons involve the use of pictures, due to the cheap availability of powerful cameras on smartphones, and the cheap availability of storage space. The rising popularity of social networking applications such as Facebook, Twitter, Instagram, and of instant messaging applications, such as WhatsApp, WeChat, is the clear evidence of this phenomenon, due to the opportunity of sharing in real-time a pictorial representation of the context each individual is living in. The media rapidly exploited this phenomenon, using the same channel, either to publish their reports, or to gather additional information on an event through the community of users. While the real-time use of images is managed through metadata associated with the image (i.e., the timestamp, the geolocation, tags, etc.), their retrieval from an archive might be far from trivial, as an image bears a rich semantic content that goes beyond the description provided by its metadata. It turns out that after more than 20 years of research on Content-Based Image Retrieval (CBIR), the giant increase in the number and variety of images available in digital format is challenging the research community. It is quite easy to see that any approach aiming at facing such challenges must rely on different image representations that need to be conveniently fused in order to adapt to the subjectivity of image semantics. This paper offers a journey through the main information fusion ingredients that a recipe for the design of a CBIR system should include to meet the demanding needs of users
Bayesian relevance feedback for content-based image retrieval
No full textDespite 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 image-based queries by asking users to mark the set of images retrieved in a neighbourhood of the query as being relevant or not. In this paper, the Bayesian decision theory is used to estimate the boundary between relevant and non-relevant images. Then, a new query is computed whose neighbourhood is likely to fall in a region of the feature space containing relevant images. The performances of the proposed query shifting method have been compared with those of other relevance feedback mechanisms described in the literature. Reported results show the superiority of the proposed method. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved
- …
