The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Evidential relational clustering using medoids

In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.Comment: in The 18th International Conference on Information Fusion, July 2015, Washington, DC, USA , Jul 2015, Washington, United State

Perception, Evidence, and our Expressive Knowledge of Others' Minds.

‘How, then, she had asked herself, did one know one thing or another thing about people, sealed as they were?’ So asks Lily Briscoe in To the Lighthouse. It is this question, rather than any concern about pretence or deception, which forms the basis for the philosophical problem of other minds. Responses to this problem have tended to cluster around two solutions: either we know others’ minds through perception; or we know others’ minds through a form of inference. In the first part of this paper I argue that this debate is best understood as concerning the question of whether our knowledge of others’ minds is based on perception or based on evidence. In the second part of the paper I suggest that our ordinary ways of thinking take our knowledge of others’ minds to be both non- evidential and non-perceptual. A satisfactory resolution to the philosophical problem of other minds thus requires us to take seriously the idea that we have a way of knowing about others’ minds which is both non-evidential and non-perceptual. I suggest that our knowledge of others’ minds which is based on their expressions – our expressive knowledge - may fit this bill