e current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD. All these techniques do not exploit spatial information, i.e. consider all the features and the corresponding descriptors without embedding their location in the image. is paper presents a new variant of the well-known VLAD (Vector of Locally Aggregated Descriptors) embedding technique which accounts, at a certain degree, for the location of features. e driving motivation comes from the observation that, usually, the most interesting part of an image (e.g., the landmark to be recognized) is almost at the center of the image, while the features at the borders are irrelevant features which do no depend on the landmark. e proposed variant, called locVLAD (location-aware VLAD), computes the mean of the two global descriptors: the VLAD executed on the entire original image, and the one computed on a cropped image which removes a certain percentage of the image borders.is simple variant shows an accuracy greater than the existing state-of-the-art approach. Experiments are conducted on two public datasets (ZuBuD and Holidays) which are used both for training and testing. Morever a more balanced version of ZuBuD is proposed. . 2017. A location-aware embedding technique for accurate landmark recognition.
This paper proposes a novel prediction tool for improving the compression performance of texture atlases. This algorithm, called Geometry-Aware (GA) intra coding, takes advantage of the topology of the associated 3D meshes, in order to reduce the redundancies in the texture map. For texture processing, the concept of the conventional intra prediction, used in video compression, has been adapted to consider neighboring information on the 3D surface. We have also studied how this prediction tool can be integrated into a complete coding solution. In particular, a block scanning strategy and a graph-based transform for residual coding have been proposed. Results show that the knowledge of the mesh topology significantly improves the compression efficiency of texture atlases 1 .
Omni-directional images are characterized by their high resolution (usually 8K) and therefore require high compression efficiency. Existing methods project the spherical content onto one or multiple planes and process the mapped content with classical 2D video coding algorithms. However, this projection induces sub-optimality. Indeed, after projection, the statistical properties of the pixels are modified, the connectivity between neighboring pixels on the sphere might be lost, and finally, the sampling is not uniform. Therefore, we propose to process uniformly distributed pixels directly on the sphere to achieve high compression efficiency. In particular, a scanning order and a prediction scheme are proposed to exploit, directly on the sphere, the statistical dependencies between the pixels. A Graph Fourier Transform is also applied to exploit local dependencies while taking into account the 3D geometry. Experimental results demonstrate that the proposed method provides up to 5.6% bitrate reduction and on average around 2% bitrate reduction over state-of-the-art methods.
Immersive visual experience can be obtained by allowing the user to navigate in a 360-degree visual content. These contents are stored in high resolution and need a lot of space on the server to store them. The transmission depends on the user's request and only the spatial region which is requested by the user is transmitted to avoid wasting network bandwidth. Therefore, storage and transmission rates are both critical. Splitting the rates into storage and transmission has not been formally considered in the literature for evaluating 360-degree content compression algorithms. In this paper, we propose a framework to evaluate the coding efficiency of 360-degree content while discriminating between storage and transmission rate and taking into account user dependency. This brings the flexibility to compare different coding methods based on the storage capacity on the server and network bandwidth of users.
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