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In this paper, we propose a tree-based multidimensional structure, GeM-Tree, which indexes both images and videos within a single general framework utilizing Earth Moverpsilas Distance. It can support different content-based image and video retrieval approaches, and can accommodate applications where the cross-similarity between images and videos need to be considered during content-based retrievals...
A novel indexing and access method, called affinity hybrid tree (AH-tree), is proposed to organize large image data sets efficiently and to support popular image access mechanisms like content-based image retrieval (CBIR) by embedding the high-level semantic image-relationship in the access mechanism as it is. AH-tree combines space-based and distance-based indexing techniques to form a hybrid structure...
A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework...
The paper proposes a method to discover effectively users' concept patterns when multiple objects of interest (e.g., foreground and background objects) are involved in content-based image retrieval. The proposed method incorporates multiple instance learning into the user relevance feedback in a seamless way to discover where the user's objects/regions of most interest are and how to map the local...
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