C. Sansone

To reject or not to reject: that is the question-an answer in case of neural classifiers

A method defining a reject option that is applicable to a given 0-reject classifier is proposed. The reject option is based on an estimate of the classification reliability, measured by a reliability evaluator /spl Psi/. Trivially, once a reject threshold /spl sigma/ has been fixed, a sample is rejected if the corresponding value of /spl Psi/ is below /spl sigma/. Obviously, as /spl sigma/ represents the least tolerable classification reliability level, when its value varies the reject option becomes more or less severe. In order to adapt the behavior of the reject option to the requirements of the considered application domain, a function P characterizing the reject options adequacy to the domain has been introduced. It is shown that P can be expressed as a function of /spl sigma/ and, consequently, the optimal value for /spl sigma/ is defined as the one which maximizes the function P. The method for determining the optimal threshold value is independent of the specific 0-reject classifier, while the definition of the reliability evaluators is related to the classifiers architecture. General criteria for defining appropriate reliability evaluators within a classification paradigm are illustrated in the paper and are based on the localization, in the feature space, of the samples that could be classified with a low reliability. The definition of the reliability evaluators for three popular architectures of neural networks (backpropagation, learning vector quantization and probabilistic network) is presented. Finally, the method has been tested with reference to a complex classification problem with data generated according to a distribution-of-distributions model.

A large database of graphs and its use for benchmarking graph isomorphism algorithms

Despite of the fact that graph-based methods are gaining more and more popularity in different scientific areas, it has to be considered that the choice of an appropriate algorithm for a given application is still the most crucial task.The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available.In this paper we present an experimental comparative evaluation of the performance of four graph matching algorithms. In order to perform this comparison, we have built and made available a large database of graphs, which is also described in detail in this article.

An Unsupervised Algorithm for Anchor Shot Detection

In this paper, we present a novel algorithm for anchor shot detection (ASD). ASD is a fundamental step for segmenting news video into stories that is among key issues for achieving efficient treatment of news-based digital libraries. The proposed algorithm firstly uses a clustering method for individuating candidate anchor shots and then employs a two-stage pruning technique for reducing the number of falsely detected anchor shots. Both clustering and pruning are carried out in an unsupervised way. The algorithm has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them

A graph-based clustering method and its applications

In this paper we present a graph-based clustering method particularly suited for dealing with data that do not come from a Gaussian or a spherical distribution. It can be used for detecting clusters of any size and shape, without the need of specifying neither the actual number of clusters nor other parameters. The method has been tested on data coming from two different computer vision applications. A comparison with other three state-of-the-art algorithms was also provided, demonstrating the effectiveness of the proposed approach.

Subgraph Transformations for the Inexact Matching of Attributed Relational Graphs

An inexact matching algorithm for Attributed Relational Graphs is presented: according to it, two graphs are considered similar if, by using a defined set of syntactic and semantic transformations, they can be made isomorphic to each other. The matching process is carried out by using a State Space Representation: a state represents a partial solution of the matching between the graphs, and a transition between two states corresponds to the addition of a new pair of matched nodes. A set of feasibility rules are introduced for pruning states associated to partial matching solutions which do not satisfy the required graphs morphism. Results outlining the computational cost reduction achieved by the method are given with reference to a set of randomly generated graphs.

Assessing the performance of a graph-based clustering algorithm

Graph-based clustering algorithms are particularly suited for dealing with data that do not come from a Gaussian or a spherical distribution. They can be used for detecting clusters of any size and shape without the need of specifying the actual number of clusters; moreover, they can be profitably used in cluster detection problems. In this paper, we propose a detailed performance evaluation of four different graph-based clustering approaches. Three of the algorithms selected for comparison have been chosen from the literature. While these algorithms do not require the setting of the number of clusters, they need, however, some parameters to be provided by the user. So, as the fourth algorithm under comparison, we propose in this paper an approach that overcomes this limitation, proving to be an effective solution in real applications where a completely unsupervised method is desirable.

A multi-stage approach for anchor shot detection

In this paper we present a novel algorithm for anchor shot detection (ASD). ASD is a fundamental step for segmenting news video into stories that is among key issues for achieving efficient treatment of news-based digital libraries. The proposed algorithm creates a set of audio/video templates of anchorperson shots in an unsupervised way, then classifies shots by comparing them to the templates. Audio similarity is evaluated by means of a new index and helps to achieve better performance than a pure video approach. The method has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them.

Combining experts for anchorperson shot detection in news videos

Automatic classification of shots extracted by news videos plays an important role in the context of news video segmentation, which is an essential step towards effective indexing of broadcasters’ digital databases. In spite of the efforts reported by the researchers involved in this field, no techniques providing fully satisfactory performance have been presented until now. In this paper, we propose a multi-expert approach for unsupervised shot classification. The proposed multi-expert system (MES) combines three algorithms that are model-free and do not require a specific training phase. In order to assess the performance of the MES, we built up a database significantly wider than those typically used in the field. Experimental results demonstrate the effectiveness of the proposed approach both in terms of shot classification and of news story detection capability.

A Multi-expert Approach for Shot Classification in News Videos

In this paper we propose a multi-expert approach for anchor shot detection in news videos. The proposed Multi-Expert System (MES) combines three algorithms selected among those presented in the literature that are model-free and do not require a specific training phase. In order to assess the performance of the proposed MES, we built up a large database, significantly wider than those ones typically used in the field. Reported experimental results show not only that proposed system performances are better than the ones of original experts but also the smart use made by the MES of strengths and weaknesses of each expert.

Unsupervised news video segmentation by combined audio-video analysis

Segmenting news video into stories is among key issues for achieving efficient treatment of news-based digital libraries. In this paper we present a novel unsupervised algorithm that combines audio and video information for automatic partitioning news videos into stories. The proposed algorithm is based on the detection of anchor shots within the video. In particular, a set of audio/video templates of anchorperson shots is first extracted in an unsupervised way, then shots are classified by comparing them to the templates using both video and audio similarity. Finally, a story is obtained by linking each anchor shot with all successive shots until another anchor shot, or the end of the news video, occurs. Audio similarity is evaluated by means of a new index and helps to achieve better performance in anchor shot detection than pure video approach. The method has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them.