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Content-based image retrieval using high-dimensional information geometry
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DOI：10.1007/s11432-014-5086-8.png)
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Abstract
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In this paper, a new content-based image retrieval approach is proposed based on high- dimensional information theory. The proposed approach overcomes the disadvantages of the current content-based image retrieval algorithms that suffer from the semantic gap. First, we present a new multidimensional information space's vector angle cosine algorithm of high-dimensional geometry, then, we provide a detailed description of our images retrieval method including proposal of an overlapping image block method and definition of a similarity degree between images on the non-dimensional information subspaces. Finally, experimental results show the higher retrieval efficiency of the proposed algorithm.
Keywords:
image retrieval
angel cosine
high-dimensional information
feature extraction
information subspace
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