With the abundance of multimedia in web databases and the increasing user need for content of many modalities, such as images, sounds, etc., new methods for retrieval and visualization of multimodal media are required. In this paper, novel techniques for retrieval and visualization of multimodal data, i.e. documents consisting of many modalities, are proposed. A novel cross-modal retrieval framework is presented, in which the results of several unimodal retrieval systems are fused into a single multimodal list by the introduction of a cross-modal distance. For the presentation of the retrieved results, a multimodal visualization framework is also proposed, which extends existing unimodal similarity-based visualization methods for multimodal data. The similarity measure between two multimodal objects is defined as the weighted sum of unimodal similarities, with the weights determined via an interactive user feedback scheme. Experimental results show that the cross-modal framework outperforms unimodal and other multimodal approaches while the visualization framework enhances existing visualization methods by efficiently exploiting multimodality and user feedback.

In this paper, we report on work around the I-SEARCH EU (FP7 ICT STREP) project whose objective is the development of a multimodal search engine. We present the project's objectives, and detail the achieved results, amongst which a Rich Unified Content Description format.

Our proposed sketch-based search and retrieval system in map databases shows the promise that intuitive search can be possible even when searching in nonannotated conventional map data. We plan to perform further work in this area to allow for more intuitive and semantic queries, applications for mobile and ubiquitous computing, and integration to existing map searching applications.

The annual SHape REtrieval Contest (SHREC) measures the performance of 3D model retrieval methods for several different types of models and retrieval purposes. In this contest the structural shape retrieval track focuses on the retrieval of 3d models which exhibit a relevant similarity in the shape structure. Shape structure is typically characterised by features like protrusions, holes and concavities. It defines relationships in which components of the shape are connected.

In this paper we present the results of the SHREC'09- Generic Shape Retrieval Contest. The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on the NIST generic shape benchmark. We hope that the NIST shape benchmark will provide valuable contributions to the 3D shape retrieval community. Seven groups have participated in the track and they have submitted 22 sets of rank lists based on different methods and parameters. The performance evaluation of the SHREC'09- Generic Shape Retrieval Contest is based on 6 different metrics.

In this paper, a novel approach for 3-D Shape description and retrieval based on the theory of ellipsoidal harmonics is presented. Four novel descriptors are introduced: the surface ellipsoidal harmonics descriptor, which concerns 3-D objects that are described as polygonal surfaces; the volumetric ellipsoidal harmonics descriptor, which is applicable to volumetric 3-D objects; the generalized ellipsoidal harmonics descriptor that is applied to any local 3-D object descriptors; and, finally, the combined ellipsoidal-spherical harmonics descriptor, which leads to a compact and powerful descriptor that inherits the advantages of both approaches: the rotation invariance properties of the spherical harmonics and the directional information enclosed in ellipsoidal harmonics. Experimental results performed using well-known 3-D object databases prove the retrieval efficiency of the proposed approach.

In this paper we present the results of the SHREC’09- Generic Shape Retrieval Contest. The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on the NIST generic shape benchmark. We hope that the NIST shape benchmark will provide valuable contributions to the 3D shape retrieval community. Seven groups have participated in the track and they have submitted 22 sets of rank lists based on different methods and parameters. The performance evaluation of the SHREC’09- Generic Shape Retrieval Contest is based on 6 different metrics.

In this paper, the novel 3D shape impact descriptor is introduced, which is based on the resulting gravitational phenomena in the surrounding area of every 3D object. The 3D object is considered as a distributed 3D mass and the descriptor of the 3D object is indirectly computed from the resulting fields. The field is described using both Newton's and general relativity's laws. In the Newtonian approach, histograms of the field values in the surrounding area of the 3D object are computed, while in the relativistic approach the descriptors are histograms of the time–space curvature in the surrounding area of the 3D object. The basic motivation behind the proposed approach is the robustness with respect to object's degeneracies and the native invariance of the resulting descriptors under rotation and translation. Experiments which were performed in various 3D object databases proved that the proposed method can be efficiently used for 3D object retrieval applications.

In this paper, a novel transform-based, blind and robust 3-D mesh watermarking scheme is presented. The 3-D surface of the mesh is firstly divided into a number of discrete continuous regions, each of which is successively sampled and mapped onto oblate spheroids, using a novel surface parameterization scheme. The embedding is performed in the spheroidal harmonic coefficients of the spheroids, using a novel embedding scheme. Changes made to the transform domain are then reversed back to the spatial domain, thus forming the watermarked 3-D mesh. The embedding scheme presented herein resembles, in principal, the ones using the multiplicative embedding rule (inherently providing high imperceptibility). The watermark detection is blind and by far more powerful than the various correlators typically incorporated by multiplicative schemes. Experimental results have shown that the proposed blind watermarking scheme is competitively robust against similarity transformations, connectivity attacks, mesh simplification and refinement, unbalanced resampling, smoothing and noise addition, even when juxtaposed to the informed ones.

This paper presents a novel framework for 3-D object content-based search and retrieval, appropriate for both partial and global matching applications. The framework is based on a graph representation of a 3-D object which is enhanced by local geometric features. The 3-D object is decomposed into meaningful parts and an attributed graph is constructed based on the connectivity of the parts. Every 3-D part is approximated with a suitable superellipsoid and a novel 3-D shape descriptor, called a 3-D distance field descriptor, is computed and associated to the corresponding graph nodes. The matching process used is based on attributed graph matching algorithm appropriate for this application. The proposed method not only provides successful retrieval results in terms of geometric similarity but also is invariant to rotation, translation and scaling of an object as well as to the different poses of articulated objects. Finally, it can be effectively used for partial and global 3-D object retrieval.

In this paper a method suitable for partial matching between 3D objects is presented. The 3D objects are firstly segmented into meaningful parts extending a method which is based on the medial surface of the objects. Then, geometric features are extracted for each part using the Spherical Trace Transform. The extracted features are combined and their covariance matrix is computed as a descriptor of each part. The contribution of the proposed approach is that a meaningful segmentation of 3D objects based on medial surface is achieved and that partial matching is performed on meaningful parts, in a rotation, translation and scaling invariant manner. The experimental results performed, proved that the proposed approach achieves accurate partial matching results in terms of distinct meaningful parts as well as satisfactory overall accuracy.

In this paper a working multimedia asset management system is presented which provides the means for storage, annotation, retrieval and re-use of any multimedia data such as 3D objects, images, sound, video and text. The working prototype is modular, customizable, secure, offers intranet and Internet connectivity and also software inter-operability as it can be compatible with various well- known, established database and ontology standards. The main innovative aspects of the proposed system are the efficient combination of the semantic aspects with multimedia data and the retrieval engine of the system which combines state-of-the-art 3D content-based algorithms and novel semantic driven, relevance feedback methods.

In this paper a novel blind watermarking method of 3D volumes based on the Weighted 3D Krawtchouk Moments is proposed. The watermark is created by a pseudo-random number generator and is embedded on low order Weighted 3D Krawtchouk Moments. The watermark detection is blind, requiring only the user’s key. The watermark bit sequence is created using the key and its cross correlation with the Weighted 3D Krawtchouk Moments of the possible watermarked volume. The proposed method is imperceivable to the user, robust to geometric transformations (translation, rotation) and to cropping attacks.