2013
DOI: 10.1186/1687-5281-2013-17
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Evaluation of noise robustness for local binary pattern descriptors in texture classification

Abstract: Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, … Show more

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Cited by 77 publications
(51 citation statements)
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“…We replicate the setup of [14]. We randomly choose one of 12 available orientations for each image and split the images into four subimages which results in 28x160x4 = 17920 images of size 288x288 which we resize to 256x256.…”
Section: Datasetsmentioning
confidence: 99%
See 1 more Smart Citation
“…We replicate the setup of [14]. We randomly choose one of 12 available orientations for each image and split the images into four subimages which results in 28x160x4 = 17920 images of size 288x288 which we resize to 256x256.…”
Section: Datasetsmentioning
confidence: 99%
“…ImNet-S2 [14] 99.8 ±0.1 [3] 75.5 ±0.8 [4] 81.5 ±2.0 [4] ---containing object-like images. This is due to the fact that the first convolution layer extracts extremely simple features (mainly edges) and acts like a Gabor filter which is not robust at classifying object datasets.…”
Section: Imnet-s1mentioning
confidence: 99%
“…As it is mentioned in some papers [55], the performance of Gabor lter and co-occurrence matrix methods signi cantly declines for noisy textures. Table 7 shows some of these results.…”
Section: Experimental Results Of the Curet Datasetmentioning
confidence: 99%
“…Some methods such as GLCM that use cooccurrence matrix are not rotation-invariant and they are sensitive to noise. Therefore, if they are used for these textures, the classi cation accuracy decreases signi cantly [55].…”
Section: Experimental Results Of the Outex Datasetmentioning
confidence: 99%
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