Efficient matching and indexing of graph models in content-basedretrieval

In retrieval from image databases, evaluation of similarity, based both on the appearance of spatial entities and on their mutual relationships, depends on content representation based on attributed relational graphs. This kind of modeling entails complex matching and indexing, which presently prevents its usage within comprehensive applications. In this paper, we provide a graph-theoretical formulation for the problem of retrieval based on the joint similarity of individual entities and of their mutual relationships and we expound its implications on indexing and matching. In particular, we propose the usage of metric indexing to organize large archives of graph models, and we propose an original look-ahead method which represents an efficient solution for the (sub)graph error correcting isomorphism problem needed to compute object distances. Analytic comparison and experimental results show that the proposed lookahead improves the state-of-the-art in state-space search methods and that the combined use of the proposed matching and indexing scheme permits for the management of the complexity of a typical application of retrieval by spatial arrangement     

Weighted walkthroughs between extended entities for retrieval by spatial arrangement

In the access to image databases, queries based on the appearing visual features of searched data reduce the gap between the user and the engineering representation. To support this access modality, image content can be modeled in terms of different types of features such as shape, texture, color, and spatial arrangement. An original framework is presented which supports quantitative nonsymbolic representation and comparison of the mutual positioning of extended nonrectangular spatial entities. Properties of the model are expounded to develop an efficient computation technique and to motivate and assess a metric of similarity for quantitative comparison of spatial relationships. Representation and comparison of binary relationships between entities is then embedded into a graph-theoretical framework supporting representation and comparison of the spatial arrangements of a picture. Two prototype applications are described.     

Merging Results for Distributed Content Based Image Retrieval

Searching information through the Internet often requires users to separately contact several digital libraries, use each library interface to author the query, analyze retrieval results and merge them with results returned by other libraries. Such a solution could be simplified by using a centralized server that acts as a gateway between the user and several distributed repositories: The centralized server receives the user query, forwards the user query to federated repositories—possibly translating the query in the specific format required by each repository—and fuses retrieved documents for presentation to the user. To accomplish these tasks efficiently, the centralized server should perform some major operations such as: resource selection, query transformation and data fusion. In this paper we report on some aspects of MIND, a system for managing distributed, heterogeneous multimedia libraries (MIND, 2001, http://www.mind-project.org). In particular, this paper focusses on the issue of fusing results returned by different image repositories. The proposed approach is based on normalization of matching scores assigned to retrieved images by individual libraries. Experimental results on a prototype system show the potential of the proposed approach with respect to traditional solutions.     

Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans

In this paper, we present a fully-automatic and real-time approach for person-independent recognition of facial expressions from dynamic sequences of 3D face scans. In the proposed solution, first a set of 3D facial landmarks are automatically detected, then the local characteristics of the face in the neighborhoods of the facial landmarks and their mutual distances are used to model the facial deformation. Training two hidden Markov models for each facial expression to be recognized, and combining them to form a multiclass classifier, an average recognition rate of 79.4 % has been obtained for the 3D dynamic sequences showing the six prototypical facial expressions of the Binghamton University 4D Facial Expression database. Comparisons with competitor approaches on the same database show that our solution is able to obtain effective results with the advantage of being capable to process facial sequences in real-time.     

Modelling Spatial Relationships between Colour Clusters

Modelling of image content based on chromatic arrangement requires suitable techniques for the representation of the spatial relationship between complex sets of pixels. We propose a model of spatial relationship between extended sets, which can be computed with the same computational complexity involved in conventional representations based on centroids, but which improves effectiveness by accounting for the overall sets of pixels involved in the relationship. The effectiveness of the proposed model is compared against the orientation between centroids in a user-based evaluation.