Local Intensity Ordering based Binary Patterns for Image Region Description

  • Rajkumar Kannan Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, India
  • Suresh Kannaiyan Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli, India
Keywords: Image region descriptor, image feature matching, local binary pattern, local intensity ordering, object recognition, texture classification

Abstract

Local image region description is a fundamental task for image feature matching in the field of Computer Vision. A good image region descriptor should have the ability to discriminate image features even though the images differ due to photometric variations and geometric transformations. Over these years, many local region descriptors have been proposed to tackle the aforementioned challenges. Achieving rotation invariance in keypoint description is considered one of the main challenges in local region description and matching. Previous approaches proposed to tackle rotation variations depend on unreliable dominant orientation estimation. In this paper, two novel local image region descriptors called Local Intensity Order-based Center Symmetric Local Binary Patterns (LIOCSLBP) and Local Intensity Order-based Orthogonally Combined Local Binary Patterns (LIOOCLBP) are proposed to build rotation invariant local region descriptions. The rotation invariance characteristic of the proposed binary pattern-based local region description is achieved by applying a simple and efficient mechanism called Local Intensity Ordering (LIO). The proposed descriptors use double interest regions for each interest point to improve feature discrimination. In order to further improve the feature discrimination ability RGBLIOCSLBP, RGBLIOOCLBP, HSVLIOCSLBP and HSVLIOOCLBP are also proposed exploiting RGB and HSV color models. Extensive experiments are conducted to evaluate the performance of the proposed descriptors on standard benchmark datasets for image matching, object recognition and scene recognition against the state-of-the-art descriptors. The experimental results show that the proposed descriptors are highly competitive to several state-of-the-art local region descriptors where the proposed descriptors outperformed the comparative approaches in many cases.


 

References

(1) Lowe D. G (2004) Distinctive image features from scale-invariant keypoints. International journal of computer vision 60(2):91-110.

(2) Zhang J, Marszałek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: A comprehensive study. International journal of computer vision 73(2):213-238.

(3) Szeliski R (2006) Image alignment and stitching: A tutorial. Foundations and Trends in Computer Graphics and Vision 2(1):1-104.

(4) Fan B, Wu F, Hu Z (2012) Rotationally invariant descriptors using intensity order pooling. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(10):2031-2045.

(5) Ke Y, Sukthankar R (2004) PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of Computer Vision and Pattern Recognition, pp 506-513.

(6) Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence 27(10):1615-1630.

(7) Bay H, Tuytelaars T, Van Gool L (2006) Surf: Speeded up robust features. In: Proceedings of European conference on computer vision, pp 404-417.

(8) Tola E, Lepetit V, Fua P (2010) Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE transactions on pattern analysis and machine intelligence 32(5):815-830.

(9) Calonder M, Lepetit V, Ozuysal M, Trzcinski T, Strecha C, Fua P (2012) BRIEF: Computing a local binary descriptor very fast. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7):1281-1298.

(10) Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: An efficient alternative to SIFT or SURF. In: 2011 International conference on computer vision, pp 564-2571.

(11) Wang Z, Fan B, Wu F (2014). Affine subspace representation for feature description. In: Proceedings of European Conference on Computer Vision, pp 94-108.

(12) Lindeberg T (1998) Feature detection with automatic scale selection. International journal of computer vision 30(2):79-116.

(13) Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. International journal of computer vision 60(1):63-86.

(14) Mikolajczyk K, et al., (2005) A comparison of affine region detectors. International journal of computer vision 65(1-2):43-72.

(15) Heikkila M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern recognition 42(3):425-436.

(16) Wang Z, Fan B, Wu F (2011) Local intensity order pattern for feature description. In: Proceedings of International Conference on Computer Vision, pp 603-610.

(17) Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern recognition 29(1):51-59.

(18) Zhu C, Bichot C. E, Chen L (2013) Image region description using orthogonal combination of local binary patterns enhanced with color information. Pattern Recognition 46(7):1949-1963.

(19) Qi X, Lu Y, Chen S, Li C. G, Guo J (2013) Spatial co-occurrence of local intensity order for face recognition. In: Proceedings of Multimedia and Expo Workshops, pp 1-6.

(20) Kang T. K, Choi I H, Lim M. T (2015) MDGHM-SURF: A robust local image descriptor based on modified discrete Gaussian–Hermite moment. Pattern Recognition 48(3):670-684.

(21) Berg A. C, Malik J (2001) Geometric blur for template matching. In: Proceedings of Computer Vision and Pattern Recognition, 1: I-607.

(22) Chen J, Shan S, Zhao G, Chen X, Gao W, Pietikainen M (2008) A robust descriptor based on weber’s law. In: Proceedings of Computer Vision and Pattern Recognition, pp 1-7.

(23) Kannan R, Ghinea G, Kannaiyan S, Swaminathan S (2014) MPRF: Multisupport polar region features for image description. In: Proceedings of International Symposium on Signal Processing and Information Technology, pp 000049-000054.

(24) Winder S. A, Brown M (2007) Learning local image descriptors. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp 1-8.

(25) Mittal A, Ramesh V (2006) An intensity-augmented ordinal measure for visual correspondence. In: Proceedings of Computer Vision and Pattern Recognition, 1:849-856.

(26) Gupta R, Patil H, Mittal A (2010) Robust order-based methods for feature description. In: Proceedings of Computer Vision and Pattern Recognition, pp 334-341.

(27) Tang F, Lim S. H, Chang N. L, Tao, H (2009) A novel feature descriptor invariant to complex brightness changes. In: Proceedings of Computer Vision and Pattern Recognition, pp 2631-2638.

(28) Kannan R, Ghinea G, Kannaiyan S, Swaminathan S (2015) CLRF: Compressed Local Retinal Features for Image Description. In: Proceedings of Advances in Pattern Recognition, pp 1-5.

(29) Dubey S. R, Singh S. K, Singh R. K (2014) Rotation and illumination invariant interleaved intensity order-based local descriptor, IEEE Transactions on Image Processing, 23(12):5323-5333.

(30) Yan P, Liang D, Tang J, Zhu M (2016) Local feature descriptor using entropy rate. Neurocomputing, 194:157-167.

(31) Yan P, Liang D, Tang J, Zhu M (2016) Local feature descriptor invariant to monotonic illumination changes. Journal of Electronic Imaging, 25(1):013023-013023.

(32) Huang D, Zhu C, Wang Y, Chen L (2014). HSOG: a novel local image descriptor based on histograms of the second-order gradients. IEEE Transactions on Image Processing, 23(11), 4680-4695.

(33) Tian T, Sethi I, Ming D, Patel N (2015) A Zoned Image Patch Permutation Descriptor. IEEE Signal Processing Letters, 22(6): 728-732.

(34) Bosch A, Zisserman A, Munoz X (2008) Scene classification using a hybrid generative/discriminative approach. IEEE transactions on pattern analysis and machine intelligence, 30(4):712-727.

(35) Verma M, Raman B (2015) Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. Journal of Visual Communication and Image Representation, 32:224-236.

(36) Dubey S. R, Singh S. K, Singh R. K (2015) Local diagonal extrema pattern: a new and efficient feature descriptor for CT image retrieval. IEEE Signal Processing Letters, 22(9):1215-1219.

(37) Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24(7):971-987.

(38) Liao S, Law M. W, Chung A. C (2009) Dominant local binary patterns for texture classification. IEEE transactions on image processing, 18(5):1107-1118.

(39) Zhao Y, Huang D. S, Jia W (2012) Completed local binary count for rotation invariant texture classification. IEEE transactions on image processing, 21(10):4492-4497.

(40) Huang M, Mu Z, Zeng H, Huang S (2015) Local image region

description using orthogonal symmetric local ternary pattern. Pattern Recognition Letters, 54:56-62.

(41) Qi X, Xiao R, Li C. G, Qiao Y, Guo J, Tang X (2014) Pairwise rotation invariant co-occurrence local binary pattern. IEEE transactions on pattern analysis and machine intelligence, 36(11):2199-2213.

(42) Yang X, Cheng K. T. T. (2014) Local difference binary for ultrafast and distinctive feature description. IEEE transactions on pattern analysis and machine intelligence, 36(1):188-194.

(43) Shang J, Chen C, Pei X, Liang H, Tang H, Sarem M, (2015) A novel local derivative quantized binary pattern for object recognition. The Visual Computer, 1-13.

(44) Li Y, Tan J, Zhong J, Chen Q (2016) Compact descriptor for local feature using dominating centre-symmetric local binary pattern. IET Computer Vision, 10(1):36-42.

(45) Abdel-Hakim, A. E, Farag A. A (2006) CSIFT: A SIFT descriptor with color invariant characteristics. In: Proceedings of Computer Vision and Pattern Recognition, 2:1978-1983.

(46) Van de Weijer J, Gevers T, Bagdanov A. D (2006) Boosting color saliency in image feature detection. IEEE transactions on pattern analysis and machine intelligence, 28(1):150-156.

(47) Burghouts G. J, Geusebroek J. M (2009) Performance evaluation of local colour invariants. Computer Vision and Image Understanding, 113(1):48-62.

(48) Van De Sande K, Gevers T, Snoek C (2010) Evaluating color descriptors for object and scene recognition. IEEE transactions on pattern analysis and machine intelligence, 32(9):1582-1596.

(49) Bach F. R, Lanckriet G. R, Jordan M. I (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the twenty-first international conference on Machine learning, (p. 6).

(50) Sivic J, Zisserman A (2003) Video Google: A text retrieval approach to object matching in videos. In: Proceedings of International Conference on Computer Vision, pp 1470-1477.

(51) Kavukcuoglu K, Sermanet P, Boureau Y. L, Gregor K, Mathieu M, Cun Y. L (2010) Learning convolutional feature hierarchies for visual recognition. In: Proceedings of Advances in neural information processing systems, pp 1090-1098.

(52) Goh H, Thome N, Cord M, Lim J. H (2012) Unsupervised and supervised visual codes with restricted boltzmann machines. In: Proceedings of European Conference on Computer Vision, pp 298-311. Springer Berlin Heidelberg.

(53) Vedaldi A, Gulshan V, Varma M, Zisserman A (2009) Multiple kernels for object detection. In: Proceedings of international conference on computer vision, pp 606-613.

(54) Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of Computer Vision and Pattern Recognition, 2:2169-2178.

(55) Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: Proceedings of Computer Vision and Pattern Recognition, pp 1-8.

(56) Arandjelovic R, Zisserman A (2013) All about VLAD. In: Proceedings of Computer Vision and Pattern Recognition, pp 1578-1585.

(57) Zhou X, Yu K, Zhang T, Huang T. S (2010) Image classification using super-vector coding of local image descriptors. In: Proceedings of European conference on computer vision, pp 141-154. Springer Berlin Heidelberg.

(58) Zhao Y, Jia W, Hu R. X, Min H (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:68-76.

(59) Lazebnik S, Schmid C, Ponce J (2005) A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8):1265-1278.

(60) He Y, Sang N, Gao C (2013) Multi-structure local binary patterns for texture classification. Pattern Analysis and Applications 16(4):595-607.

(61) Wang J. Z, Li J, Wiederhold G (2001) SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on pattern analysis and machine intelligence 23(9):947-963.

(62) Lazebnik S, Schmid C, Ponce J (2004) Semi-local affine parts for object recognition. In: Proceedings of British Machine Vision Conference, pp 779-788.

(63) Oliva A, Torralba A (2001) Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision 42(3):145-175.

(64) Cortes C, Vapnik, V (1995) Support-vector networks. Machine learning, 20(3):273-297.

Published
2017-07-13