Image Classification by Histogram Features Created with Learning Vector Quantization

Marcin Blachnik, Jorma Laaksonen

Abstract: Histogram-based data analysis is one of the most popular solutions for many problems related to image processing such as object recognition and classification. In general a histogram preserves more information from the first-order statistics of the original data than simple averaging of the raw data values. In the simplest case, a histogram model can be specified for a specific image feature type independently of any real image content. In the opposite extreme, the histograms can be made dependent not only the actual image contents, but also on the known semantic classes of the images. In this paper, we propose to use the Learning Vector Quantization (LVQ) algorithm in fine-tuning the codebook vectors for more efficient histogram creation. The performed experiments show that the accuracy of the Interest Point Local Descriptors (IPLD) feature for image classification can be improved by the proposed technique.