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Visual vocabulary serves as a fundamental component in many computer vision tasks, such as object recognition, visual search, and scene modeling. While state-of-the-art approaches build visual vocabulary based solely on visual statistics of local image patches, the correlative image labels are left unexploited in generating visual words. In this work, we present a semantic embedding framework to integrate...
This paper present a part-based approach for detecting objects with large variation of appearance. We extract local image patches as local features both from the object and from the background in training images to learn an object part model discriminatively. Our object part model discriminates the local features whether they are an object part or not. Based on the discrimination results, each local...
This paper proposes a general feature selection approach for real-time image matching systems. To demonstrate the idea??s effectiveness, we focus on the issue of rotational invariance. Most current image matching methods compute and align local image patches to a uniform dominant orientation, which are either too computationally expensive for real-time systems or insufficiently robust. In contrast...
The notion of using context information for solving high-level vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. The current literature using Markov random fields (MRFs) and conditional random fields (CRFs) often involves specific algorithm design, in which the modeling...
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