Conference Paper

A Framework for Analyzing Texture Descriptors.

Conference: VISAPP 2008: Proceedings of the Third International Conference on Computer Vision Theory and Applications, Funchal, Madeira, Portugal, January 22-25, 2008 - Volume 1
Source: DBLP


This paper presents a new unified framework for texture descriptors such as Local Binary Patterns (LBP) and Maximum Response 8 (MR8) that are based on histograms of local pixel neighborhood properties. This framework is enabled by a novel filter based approach to the LBP operator which shows that it can be seen as a special filter based texture operator. Using the proposed framework, the filters to implement LBP are shown to be both simpler and more descriptive than MR8 or Gabor filters in the texture categorization task. It is also shown that when the filter responses are quantized for histogram computation, codebook based vector quantization yields slightly better results than threshold based binning at the cost of higher computational complexity. 1

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    • "LBP can also be seen as a special filter based operator followed by a threshold quantization. The recently proposed filtering and vector quantization framework [7] was presented to treat the codebook and threshold approaches as two types of quantization of filter response and consequently allow for a systematic comparison of these two descriptors. The FLS framework is related to this work. "
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    ABSTRACT: In this paper, a Bayesian LBP operator is proposed. This operator is formulated in a novel filtering, labeling and statistic (FLS) framework for texture descriptors. In the framework, the local labeling procedure, which is a part of many popular descriptors such as LBP, SIFT and VZ, can be modeled as a probability and optimization process. This enables the use of more reliable prior and likelihood information and reduces the sensitivity to noise. The BLBP operator pursues a label image, when given the filtered vector image, by maximizing the joint probability of two images under the criterion of MAP. The proposed approach is evaluated on texture retrieval schemes using entire Brodatz database. The result reveals BLBP operator¿s efficient performance and FLS framework¿s capability to in-depth analysis of the texture descriptors on a common background.
    19th International Conference on Pattern Recognition (ICPR 2008), December 8-11, 2008, Tampa, Florida, USA; 01/2008
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    ABSTRACT: This paper presents a unified framework for image descriptors based on quantized joint distribution of filter bank responses and evaluates the significance of filter bank and vector quantizer selection. First, a filter bank based representation of the local binary pattern (LBP) operator is introduced, which shows that LBP can also be presented as an operator producing vector quantized filter bank responses. Maximum response 8 (MR8) and Gabor filters are widely used alternatives to the derivative filters which are used to implement LBP, and the performance of these three sets is compared in the texture categorization and face recognition tasks. Despite their small spatial support, the local derivative filters are shown to outperform Gabor and MR8 filters in texture categorization with the KTH-TIPS2 images. In face recognition task with CMU PIE images, the Gabor filter-based representation achieves the best recognition rate. Furthermore, it is shown that when the filter response vectors are quantized for histogram based joint density estimation, thresholding is clearly faster than using learned codebooks and, being robust to gray-level changes, it yields better recognition rate in most cases. Third, automatic selection of filter bank is discussed and excellent face recognition performance in the face recognition task is achieved with the optimized filter bank.
    Pattern Recognition Letters 03/2009; 30(4-30):368-376. DOI:10.1016/j.patrec.2008.10.012 · 1.55 Impact Factor
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    ABSTRACT: Local or global rotation invariant feature extraction has been widely used in texture classification. Local invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. This paper proposes an alternative hybrid scheme, globally rotation invariant matching with locally variant LBP texture features. Using LBP distribution, we first estimate the principal orientations of the texture image and then use them to align LBP histograms. The aligned histograms are then in turn used to measure the dissimilarity between images. A new texture descriptor, LBP variance (LBPV), is proposed to characterize the local contrast information into the one-dimensional LBP histogram. LBPV does not need any quantization and it is totally training-free. To further speed up the proposed matching scheme, we propose a method to reduce feature dimensions using distance measurement. The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.
    Pattern Recognition 03/2010; 43(3-43):706-719. DOI:10.1016/j.patcog.2009.08.017 · 3.10 Impact Factor
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