Supervised Texture Classification Using a Novel Compression-Based Similarity Measure

Autor:	Gangeh, Mehrdad J., Ghodsi, Ali, Kamel, Mohamed S.
Rok vydání:	2012
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)similarity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes. Experimental results show that the proposed approach significantly improves the performance of supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures as well as approaches performed in feature space. It also improves the computation speed by about 40% compared to its rivals. Comment: This paper has been withdrawn by the author since it has already been appeared in the proceedings of International Conference on Computer vision and Graphics (ICCVG)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1207.3071 Zobrazit plný text záznamu View this record from Arxiv