Supervised Texture Classification Using a Novel Compression-Based Similarity Measure
This work addresses texture classification for computer vision applications, but it is incremental as it builds on existing compression-based measures with a specific encoder adaptation.
The paper tackles supervised pixel-based texture classification by introducing a novel compression-based dissimilarity measure using MPEG-1 encoder, which improves performance on Brodatz and outdoor images and increases computation speed by about 40% compared to other methods.
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.