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Thanks to compact data representations and fast similarity computation, many binary code embedding techniques have been proposed for large-scale similarity search used in many computer vision applications including image retrieval. Most prior techniques have centered around optimizing a set of projections for accurate embedding. In spite of active research(More)
Many binary code embedding schemes have been actively studied recently, since they can provide efficient similarity search, and compact data representations suitable for handling large scale image databases. Existing binary code embedding techniques encode high-dimensional data by using hyperplane-based hashing functions. In this paper we propose a novel(More)
Mixed Reality (MR) has the potential to improve the quality of users' experience by immersing users in the virtual world, but the limitations of computer vision and 3D graphics techniques have made it difficult to bring up practical applications. In this paper we present a mixed reality application that combines a mixed reality experience and storytelling(More)
We present a novel, stereotype-based semantic expansion approach to identify various image sets that stereotypically represent different aspects of a given keyword. Specifically, given an adjective keyword query, our method expands it to a set of noun sub-keywords, which are stereotypical examples that can be described by the given adjective (e.g., " cute "(More)
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