Image Recognition Systems Based on Random Local Descriptors
Two image recognition systems based on Random Local Descriptors are described. Random Local Descriptors play the role of features that have to be extracted from the image. The advantage of this type of features is a possibility to create sufficiently general description of the image. This approach was tested in different image recognition tasks: handwritten digit recognition, face recognition, metal surface texture recognition and micro work piece shape recognition. The best result for handwritten digit recognition on the MNIST database is the error rate of 0.37% and for face recognition on the ORL database is the error rate of 0.1%. The results for texture and micro work piece shape recognition are also promising.
E. M. Kussul et al., "Image Recognition Systems Based on Random Local Descriptors," IEEE International Conference on Neural Networks - Conference Proceedings, pp. 2415-2450, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/IJCNN.2006.247067
2006 International Joint Conference on Neural Networks, IJCNN '06 (2006: Jul. 16-21, Vancouver, British Columbia, Canada)
Electrical and Computer Engineering
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