Tri-Chrominance Texture Pattern: A New Feature Descriptor

Authors

  • Dr I.Jeena Jacob
  • Dr K.G.Srinivasagan
  • Dr S. Gomathi
  • Ms Joyce Beryl Princess
  • Ms P.Betty
  • Mr P.Ebby Darney

DOI:

https://doi.org/10.14419/ijet.v7i2.22.11801

Published:

2018-04-20

Keywords:

Biometrics, Content-Based Image Retrieval, Feature Descriptor, Tri-Chrominance Texture Pattern

Abstract

Feature extraction plays a vital role in the information management system. This paper proposes Tri-Chrominance Texture Pattern (TCTP), a feature descriptor for extracting the features from images. This pattern helps to extract the inter-channel chrominance relationship, along with texture information of the image. The analysis were done in a natural image dataset, Corel database (DB1), pure colored texture database, Colored Brodaz Texture database (DB2) and a biometric dataset, Indian Face Image database (DB3). The proposed work outperforms the existing works in all the datasets. The analysis on DB1 shows significant improvement over the previous works like Local Binary Pattern (LBP) (78.64%/57.35%), Local Tetra Pattern (LTrP) (79.84%/56.8%) and Local Oppugnant Color Texture Pattern (LOCTP) (82.64%/58%) as 83.25%/58.2% in terms of Average Precision/ Average Recall. The analysis made in the Colored Brodaz database (DB1) shows the result of TCTP as improved from LBP (91.75%/75.18%), LTrP (91.64%/76%) and LOCTP (99.21%/89.38%) to (99.8%/93.47%).  The Average Recognition Rate (ARR) of face recognition in DB3 database using the proposed work shows considerable improvement from LBP (78.2%), LTrP (91.9%) and LOCTP (89.1%) as 88.5%. The computational complexity of the proposed work is much lesser than LTrP and LOCTP.

 

 

References

[1] Ahonen, T, Hadid, A & Pietikainen M, “Face description with local binary patterns: application to face recognitionâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no.12, (2006), pp. 2037-2041.

[2] Alvarez, S, Salvatella, A, Vanrell, M & Otazu, X, “Perceptual colour texture codebooks for retrieving in highly diverse texture datasetsâ€, Proceedings of the 20th international conference on pattern recognition, (2010), pp. 866-869.

[3] Ching, HS, Huang-Sen Chiu & Tsai-Ming, H, “An efficient image retrieval based on HSV colour spaceâ€, International Conference on Electrical and Control Engineering, (2011),pp. 5746 – 5749.

[4] Corel 1000 and Corel 10000 image database 2010. Available from:<http://www.ci.gxnu.edu.cn/cbir/Corel/1.jpg>to <http:/

/www.ci.gxnu.edu.cn/cbir/Corel/ 10000.jpg>. (22 August 2013)

[5] Deniz, O, Bueno, G, Salido, J & Dele Torre, F (2011), “Face recognition using Histograms of Oriented Gradientsâ€, Pattern Recognition Letters, vol. 32,pp. 1598–1603.

[6] Drimbarean, A & Whelan, PF (2001), “Experiments in colour texture analysisâ€, Pattern Recognition Letters, vol.22, no.1,pp. 1161–1167.

[7] Drucker,DM, Kerr,WT & Aguirre, GK (2008), “Distinguishing conjoint and independent neural tuning for stimulus features with fMRI adaptationâ€, Journal of Neurophysiology, vol.101, no.6, pp.3310-3324.

[8] Drucker, DM & Aguirre, GK (2009), “Different spatial scales of shape similarity representation in lateral and ventral loc. Cerebral Cortexâ€, Journal of Neurophysiology, vol. 19, no. 10, pp. 244 -252.

[9] Filip,J, Chantler, MJ & Haindl, M (2009), “On uniform resampling and gaze analysis of bidirectional texture functionsâ€, ACM Transactions on Applied Perception, vol.6, no. 3,pp.18-33.

[10] Gevers, T & Smeulders, A, “Pictoseek: Combining colour and shape invariant features for image retrievalâ€, IEEE Transactions on Image Processing, vol. 9, no. 1, (2000), pp.102–119.

[11] Guo, Z, Zhang, L & Zhang, D (2010a), “Rotation invariant texture classification using LBP variance with global matchingâ€, Pattern Recognition, vol. 43, no. 3, pp. 706–719.

[12] Heikkila, M, & Pietikainen, M, “A texture-based method for modeling the background and detecting moving objectsâ€, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 28, no. 4,(2006) pp. 657–662.

[13] Howe, NR & Huttenlocher, DP, “Integrating Colour, Texture and Geometry for Image Retrievalâ€, Proceedings of IEEE conference on computer vision and pattern recognition, vol. 2, (2000) pp. 239-246.

[14] Jae, YC, Yong Man Ro & Konstantinos Plataniotis, N, “Colour Local Texture Features for Colour Face Recognitionâ€, IEEE Transactions on Image Processing, vol.21, no.3,(2012), pp. 1366-1380.

[15] Jafari KK & Zadeh, H , “Radon transform orientation estimation for rotation invariant texture analysisâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6,(2005), pp. 1004-1008.

[16] Jain, V &Mukherji, Indian Face Database 2002. Available from:<http://vi-www.cs.umass.edu/~vidit/ IndianFaceDatabase>. [22 August 2013]

[17] Jeena Jacob I, K.G.Srinivasagan, K.Jayapriya, Local Oppugnant Color Texture Pattern for Image Retrieval System, Pattern Recognition Letters, (2014), 42, pp.72-78.

[18] Jonathan, B (2010), “Person Following using Histograms of Oriented Gradientsâ€, International Journal of Social Robotics, vol.2, no.2, pp. 137- 146.

[19] Kaiser, PK & Boynton, RM (1996), Human colour vision, Optical Society of America, Washington, DC.

[20] Kekre, HB, Tanuja, K, Sarode, Sudeep Thepade, D &Vaishali, S (2011), “Improved Texture Feature-Based Image Retrieval using Kekre’s Fast Codebook Generation Algorithmâ€, Proceedings of first international conference on contours of Computing Technology, Mumbai.

[21] Kokare, M, Biswas, PK & Chatterji, BN (2007), “Texture image retrieval using rotated Wavelet Filtersâ€, Pattern Recognition Letters, vol. 28, pp.1240–1249.

[22] Laine, A & Fan, J, “Texture classification by wavelet packet signaturesâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no.11, (1993) pp. 1186-1191.

[23] Lategahn, H, Gross, S, Stehle, T & Aach, T , “Texture classification by modelling joint distributions of local patterns with Gaussian mixturesâ€, IEEE Transactions on Image Processing, vol. 19, no. 6,(2010), pp. 1548–1557.

[24] Lerski, R, Straughan, K, Shad, L, Boyce, D, Bluml, S & Zuna, I (1993), “MR Image Texture Analysis – An Approach to Tissue Characterisationâ€, Magnetic Resonance Imaging, vol.11, no.6, pp. 873-887.

[25] Leo, HM & Dorothea, J (1957), “An opponent-process theory of colour visionâ€, Psychological Review, vol.64, no.6, pp. 384–404.

[26] Manjunath, BS & Ma, WY, “Texture Features for browsing and Retrieval of Image Dataâ€, IEEE Transactions on Pattern Analysis Machine Intelligence, vol.18, no.8, (1996) pp.837-842.

[27] Maenpaa, T, Ojala, T, Pietikainen, M & Soriano, M (2000a), “Robust texture classification by subsets of local binary patternsâ€, Proceedings of international conference on pattern recognition, pp. 935-938.

[28] Maenpaa, T, Pietikainen, M & Ojala, T (2000b), “Texture classification by multipredicate local binary pattern operatorsâ€, Proceedings of international conference on pattern recognition, pp. 939-942.

[29] Murala, S, R. P. Maheshwari and R. Balasubramanian, Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval, IEEE Transactions on Image Processing, (May. 2012), 21, (5), pp. 2874-2886.

[30] Ning, Z, William Cheung, K, Guoping Qiu & Xiangyang, X , “A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Taggingâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, no.7,(2011) pp.1281-1294.

[31] Noureddine, A , “Computational Perceptual Features for Texture Representation and Retrievalâ€, IEEE Transactions on Image Processing, vol.20, no.1,(2011) pp.236-246.

[32] Ojala, T, Pietikainen, M & Harwood, D (1996), “A comparative study of texture measures with classification based on feature distributionsâ€, Pattern Recognition, vol. 29, no. 1, pp. 51–59.

[33] Ojala, T & Pietikainen, M (1999), “Unsupervised texture segmentation using feature distributionsâ€, Pattern Recognition, vol. 32,no.3, pp. 477-486.

[34] Ojala, T, Pietikainen, M & Maenpaa, T, “Multiresolution gray-scale and rotation invariant texture classification with local binary patternsâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no.7, (2002),pp. 971-987.

[35] Pun CM & Lee, MC, “Log-polar wavelet energy signatures for rotation and scale invariant texture classificationâ€, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, no.5, (2003), pp. 590-603.

[36] Randen, T & Hussy, JH (1994), “Multichannel filtering for image texture segmentationâ€, Optical Engineering, vol. 33, no.8, pp. 2617-2625.

[37] Rivero-Moreno, CJ & Bres, S,“Texture feature extraction and indexing by Hermite filtersâ€, Proceeding of the 17th international conference on pattern recognition, vol. 1, (2004),pp. 684-687.

[38] Safia, A & He, D 2013, ‘New Brodatz-based Image Databases for Gray scale Colour and Multiband Texture Analysis’, ISRN Machine Vision, vol. 2013, pp.20-34. [02 December 2013]

[39] Semler, L & Dettori, L, ‘Curvelet-based texture classification of tissues in computed tomography’, Proceeding of IEEE international conference on image processing, (2006), pp. 2165-2168.

[40] Shan, C, Gong, S & Mc Owan, PW (2005), ‘Robust facial expression recognition using local binary patterns’, Proceedings of international conference on image processing, pp. 370-373.

View Full Article:

How to Cite

I.Jeena Jacob, D., K.G.Srinivasagan, D., S. Gomathi, D., Joyce Beryl Princess, M., P.Betty, M., & P.Ebby Darney, M. (2018). Tri-Chrominance Texture Pattern: A New Feature Descriptor. International Journal of Engineering & Technology, 7(2.22), 15–20. https://doi.org/10.14419/ijet.v7i2.22.11801
Received 2018-04-20
Accepted 2018-04-20
Published 2018-04-20