Noise robust and rotation invariant entropy features for texture classification

In this paper, a new formula is proposed that uses local entropy for texture feature extraction. This new method is similar to entropy; however, it calculates the local entropy of each local patch of textures. Entropy (ENT) is an attribute that measures the randomness of gray-level distribution of image. Entropy extracts dissimilarity of each local patch. In this paper, local entropy is compared to Local Binary Pattern (LBP) and local variance (VAR). All of these descriptors are rotation invariant and are used for extracting the features from each local neighborhood of textures. In spite of low accuracy of VAR and LBP the performance of ENT does not decrease significantly for noisy textures. In other words, ENT is more robust to noise than VAR and LBP. Implementations on Outex, UIUC, CUReT and MeasTex datasets show that entropy is more accurate than variance and LBP. Similar to VAR and LBP, ENT can be combined with other descriptors to improve the performance of classification. For almost all datasets that are used in implementation part, LBP/ENT is more accurate than LBP/ VAR for normal and noisy textures. Also the ENT accuracy outperforms the accuracy of VAR and LBP and most of the advanced noise robust LBP versions for low Signal to Noise Ratio (SNR) values (SNR < 10). ENT feature is a continuous value so it is necessary to quantize to discrete value for histogram. The quantization and train step of ENT is the same as VAR.