The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system.
In @cite_14 @cite_24 , a linear combination of is used to obtain a function that approximates the 3D model's surface. The weight coefficients of this function serve as components in a feature vector. On the other hand, @cite_4 proposes for 3D models, which are based on projections of the 3D model onto the faces of a circumscribing dodecahedron. Subsequently, classical CBIR techniques can be applied on the resulting images to extract feature descriptors, namely calculation of and for the resulting shape.
@cite_24 @cite_14 @cite_4
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This paper provides a content-based digital image retrieval system. Our CBIR system uses the query by example technique and the relevance feedback. A Gabor filter based image feature extraction is proposed first. Thus, 3D image feature vectors using even-symmetric 2D Gabor filters are computed for the images of a large collection and for the input image. At each step an input image is selected, from the output set obtained in the previous step, and the most similar images from the collection are retrieved. We introduce a new approach for estimating a fine grained 3D shape and continuous pose of an object from a single image. Given a training set of view exemplars, we learn and select appearance-based discriminative parts which are mapped onto the 3D model through a facility location optimization. The training set of 3D models is summarized into a set of basis shapes from which we can generalize by linear combination. Given a test image, we detect hypotheses for each part. The main challenge is to select from these hypotheses and compute the 3D pose and shape coefficients at the same time. To achieve this, we optimize a function that considers simultaneously the appearance matching of the parts as well as the geometric reprojection error. We apply the alternating direction method of multipliers (ADMM) to minimize the resulting convex function. Our main and novel contribution is the simultaneous solution for part localization and detailed 3D geometry estimation by maximizing both appearance and geometric compatibility with convex relaxation. A novel 3D face estimation method based on a regression matrix and occluding contours.3D vertices around occluding boundaries and their corresponding 2D pixel projections are highly correlated.The 3D face estimation method resembles dense surface shape recovery from missing data. This paper addresses the problem of 3D face shape approximation from occluding contours, i.e., the boundaries between the facial region and the background. To this end, a linear regression process that models the relationship between a set of 2D occluding contours and a set of 3D vertices is applied onto the corresponding training sets using Partial Least Squares. The result of this step is a regression matrix which is capable of estimating new 3D face point clouds from the out-of-training 2D Cartesian pixel positions of the selected contours. Our approach benefits from the highly correlated spaces spanned by the 3D vertices around the occluding boundaries of a face and their corresponding 2D pixel projections. As a result, the proposed method resembles dense surface shape recovery from missing data. Our technique is evaluated over four scenarios designed to investigate both the influence of the contours included in the training set and the considered number of contours. Qualitative and quantitative experiments demonstrate that using contours outperform the state of the art on the database used in this article. Even using a limited number of contours provides a useful approximation to the 3D face surface. Local descriptors are mostly extracted over the raw intensity values of the image and requires the analysis of patterns in local pixel neighborhoods. The raw images don't provide much local relationship information required for the local descriptors. To resolve this problem, a new method of local image feature description is proposed in this paper based on the patterns extracted from the multiple filtered images. The whole process is structured as follows: First, the images are filtered with the bag of filters (BoF) and then local binary pattern (LBP) is computed over each filtered image and finally concatenated to find out the single BoF-LBP descriptor. The local information enriches in the filtered images improves the discriminative ability of the final descriptor. Content based image retrieval (CBIR) experiments are performed to observe the effectiveness of the proposed approach and compared with the recent state-of-the-art methods. The experiments are conducted over four benchmark databases having natural and texture images such as Corel-1k, Corel-10k, MIT-VisTex and STex-512S. The experimental results confirm that the introduced approach is able to improve the retrieval performance of the CBIR system.