Journal of Computer Sciences and Applications
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Journal of Computer Sciences and Applications. 2015, 3(1), 1-10
DOI: 10.12691/jcsa-3-1-1
Open AccessArticle

Self-Learning of Feature Regions for Image Recognition

Satoru Yokota1, , Jiang Li1, Yuichi Ogishima1, Hiromasa Kubo1, Hakaru Tamukoh2, and Masatoshi Sekine1

1Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan

2Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

Pub. Date: January 22, 2015

Cite this paper:
Satoru Yokota, Jiang Li, Yuichi Ogishima, Hiromasa Kubo, Hakaru Tamukoh and Masatoshi Sekine. Self-Learning of Feature Regions for Image Recognition. Journal of Computer Sciences and Applications. 2015; 3(1):1-10. doi: 10.12691/jcsa-3-1-1


Mobile systems are used in various environments. Thus, it is practical for image recognition systems to autonomously learn template images that are adaptive to objects in their various environments. However, learning the features of such objects requires large-scale computation and complex control. Hence, we propose an image recognition system that selects and learns regions that have a given object's features. This system is designed as a hardware/software (hw/sw) complex system with the multi-dimensional field programmable gate array (FPGA) “Vocalise.” This study discusses the possibility of dynamically building image databases and of real-time learning using the proposed image recognition system. Results indicate that the learning speed of the proposed method is estimated to be 1.4 × 103 faster than that obtained with a conventional software method. This suggests the possibility of real-time learning.

autonomous learning feature region hw/sw complex system image recognition vocalise

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