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In order to mimic the representation of textual documents, some approaches have recently been proposed to represent visual contents in terms of visual words in many applications such as object recognition and image annotation. In this paper, we propose to build an effective visual vocabulary by using Hierarchical Gaussian Mixture model instead of traditional clustering methods. In addition, Probabilistic...
Many object descriptors have been proposed in the state of the art. For many reasons (occlusion, point of view, acquisition conditions...), local descriptors have a better robustness for image understanding applications. The goal of this paper is to make a comparative study of eight recent local descriptors. The objective is here to quantify their ability to generate automatically an object taxonomy...
This paper describes how to construct a hyper-graph model from a large corpus of multi-view images using local invariant features. We commence by representing each image with a graph, which is constructed from a group of selected SIFT features. We then propose a new pairwise clustering method based on a graph matching similarity measure. The positive example graphs of a specific class accompanied...
In this paper, we present an efficient and robust subspace learning based object tracking algorithm with special illumination handling. Illumination variances pose a great challenge to most of object tracking algorithms. In this paper, an edge orientation based feature has been proposed and proven to approximately invariant to illumination changes. Besides, we utilize the incremental subspace learning...
Viewpoint Space Partition based on Aspect Graph is one of the core techniques of 3D object recognition. Projection images obtained from critical viewpoint following this approach can efficiently provide topological information of an object. Computational complexity has been a huge challenge for obtaining the representation viewpoints used in 3D recognition. In this paper, we discuss inefficiency of...
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