Compression of pre-computed per-pixel texture features using MDS

Abstract

There are many successful experiences employing texture analysis to improve the accuracy and robustness of image segmentation. Usually, per-pixel based texture analysis is required, which involves intensive computation especially for large images. Precomputation and storing of the texture features involves large file space which is not cost effective. To adopt to these novel needs, we propose the use of multidimensional scaling (MDS) technique to reduce the size of per-pixel texture features of an image while preserving the textural discrminiability for segmentation. Per-pixel texture features will create very large dissimilarity matrix, making the solving of MDS intractable. A sampling-based MDS is therefore introduced to tackle the problem with a divide-and-conquer approach. A compression ratio of 1:24 can be achieved with an average error rate lower than 7%. Preliminary experiments on segmentation using the compressed data show satisfactory results as good as using the uncompressed features. We foresee that such a method will allow texture features to be stored and transferred more efficiently on low processing power devices or embedded systems like mobile phones.