Local ZigZag Max histograms of pooling pattern for texture classification

Electronics LettersVolume 55, Issue 7 p. 382-384 Image and vision processing and display technologyFree Access Local ZigZag Max histograms of pooling pattern for texture classification Swalpa Kumar Roy, Corresponding Author Swalpa Kumar Roy swalpa@cse.jgec.ac.in orcid.org/0000-0002-6580-3977 Computer Vision & Pattern Recognition Unit, Indian Statistical Institute, Kolkata –, 700108 IndiaSearch for more papers by this authorShiv Ram Dubey, Shiv Ram Dubey Computer Vision Group, Indian Institute of Information Technology, Sri City, Andhra Pradesh, 517646 IndiaSearch for more papers by this authorBidyut B. Chaudhuri, Bidyut B. Chaudhuri Computer Vision & Pattern Recognition Unit, Indian Statistical Institute, Kolkata –, 700108 IndiaSearch for more papers by this author Swalpa Kumar Roy, Corresponding Author Swalpa Kumar Roy swalpa@cse.jgec.ac.in orcid.org/0000-0002-6580-3977 Computer Vision & Pattern Recognition Unit, Indian Statistical Institute, Kolkata –, 700108 IndiaSearch for more papers by this authorShiv Ram Dubey, Shiv Ram Dubey Computer Vision Group, Indian Institute of Information Technology, Sri City, Andhra Pradesh, 517646 IndiaSearch for more papers by this authorBidyut B. Chaudhuri, Bidyut B. Chaudhuri Computer Vision & Pattern Recognition Unit, Indian Statistical Institute, Kolkata –, 700108 IndiaSearch for more papers by this author First published: 01 April 2019 https://doi.org/10.1049/el.2018.7631Citations: 3AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The efficiency of any texture classification model confides on descriptor used for similarity matching. The formation of image descriptor is a challenging and important task in computer vision. This Letter introduces a local ZigZag Max histograms of pooling pattern (LZMHPP) for classification of texture images. To compute the descriptor, first the dissimilarity between centre pixel and its neighbours is computed for each image patch over the whole image, then the dissimilarity map is encoded with different type of ZigZag ordering mechanism, and finally the Max histograms pooling is used to form LZMHPP descriptor from two complementary ZigZag weighted structures and achieves sufficient robustness under geometric variations. The experimental study on KTH-TIPS and CUReT texture databases indicates the efficiency and supremacy of LZMHPP descriptor for texture classification. Introduction The texture recognition plays a vital role in many applications such as industrial image analysis, remote sensing image analysis, facial analysis, and so on. The texture classification from images is a very challenging problem due to the huge amount of inter-class similarities. The primary method to deal with this problem is to match the descriptors computed from the texture images instead of image itself, as the descriptors exhibit the robustness and discriminative features. Several methods were proposed in the past to design the suitable image descriptors for the texture characterisation. Local binary pattern (LBP) is one of state-of-the-art descriptor for texture classification [1]. Several improvements over LBP have been proposed such as local ternary pattern (LTP) [2], local derivative pattern (LDP) [3], local tetra pattern (LTrP) [4], local bit-plane dissimilarity pattern [5], complete dual-cross pattern [6], and so on. Recently, ZigZag based descriptor has shown a great improvement in the performance using local directional ZigZag pattern (LDZP) [7]. The existing descriptors suffer due to the limited descriminative power and robustness ability. In this Letter, a new descriptor is proposed by exploiting the benefits of ZigZag features as well as Max histogram pooling. Basically, first different local ZigZag patterns are extracted, then the Max pooling is applied over the histograms of different local ZigZag patterns. The utilisation of different ZigZag features boosts the discriminative ability, whereas the Max pooling of histograms introduces more robustness against the various geometrical variations such as rotation, scaling, and so on. Local ZigZag max histograms of pooling pattern The LBP [1] is a well-known and highly discriminative texture descriptor which labels each pixel within a local neighbourhood at a radius around its centre by computing the sign of difference of the intensity values of neighbours from the centre. The LBP achieves enough discriminate under different geometric variations. However, in LBP structure, there is no perceptual angular relation between two consecutive neighbouring samples with respect to its centre. So, it cannot capture enough angular information in the local texture patch which could be useful to capture the complex local structure. To overcome the aforementioned problems, we have designed a new descriptor in this Letter based on local ZigZag weighed structure [7]. In LZP descriptor, the sign of intensity differences of the centre to its neighbouring pixels are calculated to label each pixel initially as (1)where represents a vector of local intensity difference, which produces LZP invariant under monotonic photometric changes, , and N represent the pixel intensity of centre, neighbouring and total number of involved points (i.e, ), respectively. The value of LZP at is calculated as (2) The LZP code generates distinct decimal values from the sign of the differences between centre and its neighbourhoods and generally described by the string of N-bit binary, where represents the weights of the nth bit. Since, we calculate LZP by the thresholding within a window, the LZP output ranges between 0 and 255. Due to the visibility of angular relationship from second to the seventh pixel of the ZigZag structure of Fig. 1, the two neighbours of each pixel extend the angle by either or or . While in LBP, the angular relation between any two successive sample point with respect to its reference pixel is always to be a constant ( for 8 sample points) and shows no visually perceptible angular relationship between two alternate pixel with respect to their intermediate. These fluctuations of LZP angles among the sample points allow to capture more frequent changes in local micro-texture pattern. This micro-structure forms four different orders of local ZigZag kernel based on their starting point shown in Figs. 1a–d, and which may make LZP as a better rotational invariant texture descriptor than traditional descriptors. Fig 1Open in figure viewerPowerPoint Variants of local ZigZag structure (3 3) a–d Four different () orders First, the four different LZP maps are extracted from the input texture image I (Fig. 2a) using the four different orders ZigZag structure so, called , and (Figs. 2b and c). Then, the extracted LZP maps are used to form two groups of complementary local ZigZag Patterns (CLZPs), i.e. and by stacking the output of two complementary ZigZag structure together as shown in Figs. 2d and e and represented as (3) Fig 2Open in figure viewerPowerPoint Proposed framework a Texture image b and c Different local ZigZag structures are stacked to form two complementary LZP groups, i.e. () and () where , and are shown in Fig. 1 d and e Two complementary LZP maps f and g LZMH is build from CLZP h Final LZMHPP histogram In this context, the complementary ZigZag structure indicates that it is the rotated version of another one, which allows to encode the complementary rotational invariant texture information. After that, the local grey scale distribution of s for each pixel (x, y) of the texture image I with dimension is computed. It is indexed by building a discrete distribution of bins of LZP codes, more formally the histogram is defined by (4) where , and represents the maximum value of CLZP pattern. The histogram codes of and for each reference bins are calculated using a max histogram pooling () operation between two and represented as (see Figs. 2f and g) (5)The final local ZigZag Max histograms of pooling pattern ( descriptor as shown in Fig. 2h is formed by concatenating the two codes generated by and as (6)The LZMHPP is efficient as it only doubles the dimension of basic LZP. Experimental results and discussions To inspect the texture classification performance of the proposed descriptor, the experiments are carried out on two commonly used well-known publicly available texture databases, namely KTH-TIPS [8] and CUReT [9], respectively. The CUReT database [9, 10] is designed to contain large intra-class variation and is widely used to estimate the classification performance. It contains 61 texture classes with 92 images per class, which are cropped into regions and converted to grey scale. The images are captured under different illumination and viewing directions with constant scale. The KTH-TIPS database [8] is extended by introducing new samples of ten CUReT textures. It contains texture images with three different poses, four illuminations, and nine different scales of size and where each class contains 81 samples. The performance of proposed LZMHPP descriptor is evaluated using the parametric multiclass support vector machine (LIBSVM [11]) classifier. The classification performance is computed as the average of K-fold () cross-validation. The estimated classification accuracy of proposed LZMHPP descriptor is compared with the several state-of-the-art texture descriptors, like, LBP [1], LTP [2], LDP [3], LTrP [4], and LDZP [7] in Table 1. The LZMHPP has better classification rates for both the texture databases compared to the existing texture descriptors. This is because LZMHPP encodes the local ZigZag relationship and extracts the encoded features using Max pooling strategy. The discriminative ability is increased by encoding the local relation through multiple ZigZag fashion. The robustness of descriptor is increased by applying the Max pooling operation. It is evident that the proposed descriptor outperforms other existing methods and achieves classification rates of and for KTH-TIPS and CUReT dataset, respectively. In addition to classifier, the performance of the proposed descriptor is also evaluated using multilayer perceptron () and achieves comparable classification rates and for KTH-TIPS and CUReT datasets, respectively. It shows that outperforms in the proposed framework. Table 1 also shows the length of the different feature vectors including proposed one. It can be observed that the length of the proposed descriptor is lower or comparable to , , and descriptors. Table 1. Texture classification performance on different texture descriptors Dataset Classifier Accuracy Feature length KTH-TIPS CUReT LBP SVM 97.75 98.10 256 LTP 97.87 98.02 512 LDP 97.92 98.08 1024 LTrP MLP 97.90 98.00 767 LDZP 98.00 98.17 354 LZMHPP 98.75 98.38 512 LZMHPP 98.30 98.20 512 In the proposed framework (shown in Fig. 2), the complementary maps are stacked together. We performed an experiment to find out the best suitable pairs together to maximise the utilisation of complementary information. There are three different ways, we can choose the ZigZag structure to form two independent groups taken from a total four available structures (i.e. , , , and ) (Fig. 1). We have used following three possible combinations, and . The classification performance for three different descriptors is shown in Table 2. It can be observed from this result that among three possible combinations, the descriptor outperforms others. It shows that the groups and have the highest complementary information in its maps. The performance of the proposed descriptor is also compared with existing descriptors using cumulative matching characteristic (CMC) curve shown in Fig. 3. The CMC curves also show that the proposed descriptor outperforms the state-of-the-art descriptors. Table 2. SVM accuracy of LZMHPP with different ZigZag combinations Descriptors KTH-TIPS CUReT 97.50 0.824 97.41 0.612 97.30 0.560 97.50 0.470 98.75 0.621 98.38 0.524 Fig 3Open in figure viewerPowerPoint CMC curve for classification performance of descriptors on a KTH-TIPS database b CUReT database Conclusion This Letter proposed a new texture descriptor named as LZMHPP by exploiting the max pooling over the histograms of features of several local ZigZag relationships. The utilisation of multiple ZigZag features boosts the discriminative power and max pooling of histograms increases the robustness of the proposed descriptor against geometrical changes. The proposed descriptor is tested over two benchmark texture datasets and the results are compared with state-of-the-art descriptors. Very promising performance is observed for the proposed LZMHPP descriptor. References 1Ojala, T., Pietikainen, M., Maenpaa, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971– 987 (https://doi/org/10.1109/TPAMI.2002.1017623) 2Tan, X., Triggs, B.: ‘Enhanced local texture feature sets for face recognition under difficult lighting conditions’, IEEE Trans. Image Process., 2010, 19, (6), pp. 1635– 1650 (https://doi/org/10.1109/TIP.2010.2042645) 3Zhang, B., Gao, Y., Zhao, S. et. al.,: ‘Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor’, IEEE Trans. Image Process., 2010, 19, (2), pp. 533– 544 (https://doi/org/10.1109/TIP.2009.2035882) 4Murala, S., Maheshwari, R., Balasubramanian, R.: ‘Local tetra patterns: a new feature descriptor for content-based image retrieval’, IEEE Trans. Image Process., 2012, 21, (5), pp. 2874– 2886 (https://doi/org/10.1109/TIP.2012.2188809) 5Dubey, S.R., Singh, S.K., Singh, R.K.: ‘Novel local bit-plane dissimilarity pattern for computed tomography image retrieval’, Electron. Lett., 2016, 52, (15), pp. 1290– 1292 (https://doi/org/10.1049/el.2016.1206) 6Roy, S.K., Chanda, B., Chaudhuri, B.B. et. al.,: ‘ A complete dual-cross pattern for unconstrained texture classification’. IEEE 4th IAPR Asian Conf. on Pattern Recognition (ACPR), Nanjing, China, November 2017, pp. 741– 746 7Roy, S.K., Chanda, B., Chaudhuri, B.B. et. al.,: ‘Local directional ZigZag pattern: a rotation invariant descriptor for texture classification’, Pattern Recognit. Lett., 2018, 108, pp. 23– 30 (https://doi/org/10.1016/j.patrec.2018.02.027) 8Hayman, E., Caputo, B., Fritz, M. et. al.,: ‘ On the significance of real-world conditions for material classification’. European Conf. on Computer Vision (ECCV), Prague, Czech Republic, May 2004, pp. 253– 266 9Dana, K.J., Van Ginneken, B., Nayar, S.K. et. al.,: ‘Reflectance and texture of real-world surfaces’, Int. J. Comput. Vis., 1999, 18, (1), pp. 1– 34 (https://doi/org/10.1016/S0262-8856(99)00008-6) 10Zhang, J., Marszałek, M., Lazebnik, S. et. al.,: ‘Local features and kernels for classification of texture and object categories: a comprehensive study’, IJCV, 2007, 73, (2), pp. 213– 238 (https://doi/org/10.1007/s11263-006-9794-4) 11Chang, C.C., Lin, C.J.: ‘Libsvm: a library for support vector machines’, ACM Trans. Intell. Syst. Technol. (TIST), 2011, 2, (3), p. 27 Citing Literature Volume55, Issue7April 2019Pages 382-384 FiguresReferencesRelatedInformation