Image annotation based on learning vector quantization and localized Haar wavelet transform features

Matthias Blume; Dan R. Ballard

doi:10.1117/12.271478

4 April 1997 Image annotation based on learning vector quantization and localized Haar wavelet transform features

Matthias Blume, Dan R. Ballard

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271478
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

This paper describes a method for classifying image pixels as belongings to one of several known classes or as being unrecognized. A Haar wavelet transform is utilized to generate a feature vector per image pixel. This provides information about the local brightness and color, as well as about the texture of the surrounding area. Hand-labeled images are used to generate a codebook using the Optimal Learning Rate Learning Vector Quantization algorithm. The system is applied to outdoor images recorded with a video camera from a moving automobile. The classification is used to produce annotated images, in which recognized areas are replaced by uniform colors denoting the corresponding class, and unrecognized areas are left unchanged. For small numbers of classes, the pixel classification accuracy is as high as 99%.

Citation Download Citation

Matthias Blume and Dan R. Ballard "Image annotation based on learning vector quantization and localized Haar wavelet transform features", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271478

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