At the 2010 annual ACM Conference on Recommender Systems (RecSys 2010) a panel addressed emerging topics regarding recommender systems as a whole and specifically their role in industry. This report summarizes answers from a distinguished group of industry leaders representing different industries in which recommender systems are highly relevant. Panel members discuss questions regarding the role of recommender systems in their own industry area, killer applications, opportunities, and future directions.

Although powerful image representations have been proposed for content-based image retrieval, most of the current systems are "rigid", i.e. they retrieve a fixed set of images as response to a given query and an image feature. In this paper, our goal is to introduce tools for making image retrieval systems more flexible. More precisely, we use multiple image features, and present in details a new relevance feedback technique that integrates the positive and negative examples provided by the user. Experimental results on various large databases show that the proposed technique is more performant than the standard relevance feedback approach.

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Relevance feedback is one of the strong components of Surfimage, the INRIA content-based image retrieval system. Relevance feedback is about learning from user interaction, and is useful in tasks like query refinement and multiple queries. We present two relevance feedback techniques currently implemented in Surfimage.

We describe a novel technique for face recognition based on deformable intensity surfaces which incorporates both the shape and texture components of the 2D image. The intensity surface of the facial image is modeled as a deformable 3D mesh in (z, y, I(x, y)) space. Using an efficient technique for matching two surfaces (in terms of the analytic modes of vibration), we obtain a dense correspondence field (or 3D warp) between two images. The probability distributions of two classes of warps are then estimated from training data: interpersonal and extrapersonal variations. These densities are then used in a Bayesian framework for image matching and recognition. Experimental results with facial data from the US Army FERET database demonstrate an increased recognition rate over the previous best methods.

We propose a probabilistic similarity measure for direct image matching based on a Bayesian analysis of image deformations. We model two classes of variation in object appearance: intra-object and extra-object. The probability density functions for each class are then estimated from training data and used to compute a similarity measure based on the a posteriori probabilities. Furthermore, we use a novel representation for characterizing image differences using a deformable technique for obtaining pixel-wise correspondences. This representation, which is based on a deformable 3D mesh in XYI-space, is then experimentally compared with two simpler representations: intensity differences and optical flow. The performance advantage of our deformable matching technique is demonstrated using a typically hard test set drawn from the US Army's FERET face database.

We present a method for nonrigid motion analysis in time sequences of volume images (4D data). In this method, nonrigid motion of the deforming object contour is dynamically approximated by a physically-based deformable surface. In order to reduce the number of parameters describing the deformation, we make use of a modal analysis which provides a spatial smoothing of the surface. The deformation spectrum, which outlines the main excited modes, can be efficiently used for deformation comparison. Fourier analysis on time signals of the main deformation spectrum components provides a temporal smoothing of the data. Thus a complex nonrigid deformation is described by only a few parameters: the main excited modes and the main Fourier harmonics. Therefore, 4D data can be analyzed in a very concise manner. The power and robustness of the approach is illustrated by various results on medical data. We believe that our method has important applications in automatic diagnosis of heart diseases and in motion compression.

The authors present a physically based deformable model which can be used to track and analyze non-rigid motion of dynamic structures in time sequences of 2-D or 3-D medical images. The model considers an object undergoing an elastic deformation as a set of masses linked by springs, where the natural length of the springs is set equal to zero and is replaced by a set of constant equilibrium forces, which characterize the shape of the elastic structure in the absence of external forces. This model has the extremely nice property of yielding dynamic equations which are linear and decoupled for each coordinate, irrespective of the amplitude of the deformation. It provides a reduced algorithmic complexity, and a sound framework for modal analysis, which allows a compact representation of a general deformation by a reduced number of parameters. The power of the approach to segment, track and analyze 2-D and 3-D images is demonstrated by a set of experimental results on various complex medical images.>