Interactive Data Mining for Large-Scale Image Databases Based on Formal Concept Analysis
Takanari Tanabata*, Kazuhito Sawase**, Hajime Nobuhara**,
and Barnabas Bede***
*National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
**Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenodai, Tsukuba Science City, Ibaraki 305-8573, Japan
***Department of Mathematics, The University of Texas-Pan American, 1201 West University, Edinburg, Texas 78539, USA
In order to perform an interactive data-mining for huge image databases efficiently, a visualization interface based on Formal Concept Analysis (FCA) is proposed. The proposed interface system provides an intuitive lattice structure enabling users freely and easily to select FCA attributes and to view different aspects of the Hasse diagram of the lattice of a given image database. The investigation environment is implemented using C++ and the OpenCV library on a personal computer (CPU = 2.13 GHz, MM = 2 GB). In visualization experiments using 1,000 Corel Image Gallery images, we test image features such as color, edge, and face detectors as FCA attributes. Experimental analysis confirms the effectiveness of the proposed interface and its potential as an efficient datamining tool.
and Barnabas Bede, “Interactive Data Mining for Large-Scale Image Databases Based on Formal Concept Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.3, pp. 303-308, 2010.
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