Abstract
In this paper, a novel feature descriptor named Z with Tilted Z Local Binary Pattern (Z⊕TZLBP) is proposed for extracting coral reef image features efficiently. The aim is to reduce LBP’s computational complexity by reducing the size of the feature vector. This is achieved in the proposed Z⊕TZLBP by dividing the neighborhood pixels into two non overlapped groups of Z and TZ (Tilted Z), and computing LBP wherein the centre pixel is also treated as one of the neighbors. KNN classification with four different distance metrices has been used for classification purpose. Metric F-measure is used to evaluate the performance of the proposed system. Experiments conducted with various coral image and video data sets show that the proposed feature descriptor outperforms Local Binary Pattern (LBP), Uniform Pattern, Center-Symmetric Local Binary Pattern and Orthogonal Combination of Local Binary Pattern and also guarantees accurate and efficient feature extraction.
Similar content being viewed by others
References
Rohwer, F., Youle, M., & Vosten, D. (2010). Coral reefs in the microbial seas (Vol. 1). Basalt: Plaid Press.
Zhou, X., Sanchez, S. A., & Kuijper, A. (2010). 3D face recognition with local binary patterns. In Sixth international conference on intelligent information hiding and multimedia signal processing.
Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.
Shan, C., Gong, S., & McOwan, P. W. (2009). Facial expression recognition based on Local Binary Patterns: A comprehensive study. Elsevier, Image and Vision Computing, 27, 803–816.
Zhao, G., & Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915–928.
Ojala, T., Pietikaeinen, M., & Maeenpaea, 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.
Topi, M., Timo, O., Matti, P., & Maricor, S. (2010). Robust texture classification by subsets of local binary patterns. In IEEE.
Topi, M., Matti, P., & Timo, O. (2010). Texture classification by multi-predicate local binary pattern operators. In IEEE.
Ojala, T., & Pietikainen, M. (1999). Unsupervised texture segmentation using feature distributions. Pattern Recognition, 32, 477–486.
Shihavuddin, A. S. M., Gracias, N., Garcia, R., Gleason, A. C. R., & Gintert, B. (2013). Image-based coral reef classification and thematic mapping. Remote Sensing, 5, 1809–1841.
Marcos, M. S. A., David, L., Peñaflor, E., Ticzon, V., & Soriano, M. (2008). Automated benthic counting of living and non-living components in Ngedarrak Reef, Palau via subsurface underwater video. Environmental Monitoring Assess, 145, 177–184.
Clement, R., Dunbabin, M., & Wyeth, G. (2005). Toward robust image detection of crown-of-thorns starfish for autonomous population monitoring. In Conference on robotics and automation.
Soriano, M., Marcos, S., Saloma, C., Quibilan, M., & Aliiio, P. (2001). Image classification of coral reef components from underwater color video. In OCEANS, MTS/IEEE conference and exhibition (Vol. 2), pp. 1008–1013.
Heikkil, M., Pietikainena, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42, 425–436.
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, 1949–1963.
Andono, P. N., Purnama, K. E., & Hariadi, M. (2013). Underwater image enhancement using adaptive filtering for enhanced SIFT-based image matching. Journal of Theoretical and Applied Information Technology, 52(3), 1–8.
Iqbal, K., Salam, R. A., Osman, A., & Talib, A. Z. (2012). Underwater image enhancement using an integrated colour model. IAENG, International Journal of Computer Science, 34(2), 239–244.
Iqbal, K., Odetayo, M., James, A., Salam, R. A., & Talib, A. Z. H. (2010.). Enhancing the low quality images using unsupervised colour correction method. In IEEE international conference on systems man and cybernetics (SMC) (Vol. 22).
Mahiddine, A., Seinturier, J., Boï, J.-M., Drap, P., & Merad, D. (2012). Performances analysis of underwater image pre-processing techniques on the repeatability of SIFT and SURF descriptors. In 20th international conference in central Europe on computer graphics, visualization and computer vision, WSCG.
Padmavathi, G., Subashini, P., Kumar, M. M., & Thakur, S. K. (2010). Comparison of filters used for underwater image pre-processing. IJCSNS International Journal of Computer Science and Network Security, 10(1), 58–65.
Singh, B., Mishra, R. S., & Gour, P. (2011). Analysis of contrast enhancement techniques for underwater image. International Journal of Computer Technology and Electronics Engineering (IJCTEE), 1(2), 190–194.
Blanchet, J.-N., Déry, S., Landry, J.-A., & Osborne, K. (2016). Automated annotation of corals in natural scene images using multiple texture representations. PeerJ Preprints, 4, e2026v2.
Kohler, K. E., & Gill, S. M. (2006). Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. Computers & Geosciences, 32, 1259–1269.
Beijbomy, O., Edmunds, P. J., Kline, D. I., Mitchell, B. G., & Kriegman, D. (2012). Automated annotation of coral reef survey images. In IEEE conference on computer vision and pattern recognition (CVPR), Providence, Rhode Island, pp. 16–21.
Tusa, E., Reynolds, A., Lane, D. M., Villegas, H., & Bosnjak, A. (2014). Implementation of a fast coral detector using a supervised machine learning and gabor wavelet feature descriptors. In Sensor systems for a changing ocean (SSCO), IEEE, pp. 1–6.
Marcos, M. S. A., Soriano, M., & Saloma, C. (2003). Low-level color and texture feature extraction of coral reef components. Science Diliman, 15(1), 45–50.
Kumar Rai, R., Gour, P., & Singh, B. (2012). Underwater image segmentation using CLAHE enhancement and thresholding. International Journal of Emerging Technology and Advanced Engineering, 2(1), 118–123.
Bhalodiya, K. J., & Doshi, K. (2014). Performance evaluation of different segmentation techniques for underwater and arial images. International Journal of Research in Computer and Communication Technology, 3(1), 172–180.
Zhang, J., & Hu, J. (2008). Image segmentation based on 2D Otsu method with histogram analysis. In International conference on computer science and software engineering.
Jamshidi, O., & Pilevar, A. H. (2013). Automatic segmentation of medical images using fuzzy c-means and the genetic algorithm. Journal of Computational Medicine. doi:10.1155/2013/972970.
Padmavathi, G., Muthukumar, M., & Thakur, S. K. (2010). Non linear Image segmentation using fuzzy c means clustering method with thresholding for underwater images. IJCSI, International Journal of Computer Science Issues, 7(3), 9.
Liu, Z., Sang, E., & Liao, Z. (2005). Underwater acoustic image segmentation using neural deformable template. In Proceedings of the IEEE international conference on mechatronics and automation, Niagara Falls, Canada.
Pizarro, O., Rigby, P., & Colquhoun. J. (2008). Towards image-based marine habitat classification. In IEEE.
Padmavathi, G., Muthukumar, M., & Thakur, S. K. (2010). Kernel principal component analysis feature detection and classification for underwater images. In 3rd international congress on image and signal processing (CISP).
Pican, N., Trucco, E., Ross, M., Lane, D. M., Petillot, Y., & Ruiz, I. T. (1998). Texture analysis for seabed classification: Co-occurrence matrices vs self-organizing maps. In IEEE.
Mehta, A., Ribeiro, E., Gilner, J., & van Woesik, R. (2007). Coral reef texture classification using support vector machines. In International conference on computer vision theory and applications, Barcelona, Spain.
Bala, A., & Kaur, T. (2016). Local texton XOR patterns: A new feature descriptor for content based image retrieval. Engineering Science and Technology, An International Journal, 19, 101–112.
Omranpour, H., & Ghidary, S. S. (2015). A heuristic supervised Euclidean data difference dimension reduction for KNN classifier and its application to visual place classification. Neural Computing & Applications, 27(7), 1867–1881.
Stokes, M. D., & Deane, G. B. (2009). Automated processing of coral reef benthic images. Limnology and Oceanography: Methods, 7, 157–168.
Botelho, S. S. C., Drews Junior, P. L. J., Figueiredo, M. D. S., Rocha, C. H. D., & Oliveira, G. L. (2009). Appearance-based odometry and mapping with feature descriptors for underwater robots. Journal of the Brazilian Computer Society, 15(3), 47–54.
Ke, Y., & Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition, CVPR.
Lal, S., & Chandra, M. (2014). Efficient algorithm for contrast enhancement of natural images. The International Arab Journal of Information Technology, 11(1), 95–102.
Singh, R. P., & Dixit, M. (2015). Histogram equalization: A strong technique for image enhancement. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(8), 345–352.
Sheena, C. V., & Narayanan, N. K. (2015). Key-frame extraction by analysis of histograms of video frames using statistical methods. In 4th international conference on eco-friendly computing and communication systems, Procedia computer science, Vol. 70, pp. 36–40.
Dang, C. T., Kumar, M., & Radha, H. (2012). Key frame extraction from consumer videos using epitome. In IEEE.
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Jayapriya, K., Ani Brown Mary, N., & Rajesh, R. S. (2015). Cloud service recommendation based on a correlated QoS ranking prediction. Journal of Network and Systems Management, 24(4), 916–943.
Wagsta, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means clustering with background knowledge. In Eighteenth international conference on machine learning, pp. 577–584.
Grover, N. (2014). A study of various Fuzzy Clustering Algorithms. International Journal of Engineering Research, 3(3), 177–181.
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621.
Hayman, E., Caputo, B., Fritz, M., & Eklundh, J.-O. (2004). On the significance of real-world conditions for material classification. In Computer vision, Springer.
Loya, Y. (2004). The coral reefs of Eilat—Past, present and future: Three decades of coral community structure studies. In E. Rosenberg & Y. Loya (Eds.), Coral health and disease (pp. 1–34). Berlin: Springer.
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.
Dana, K. J., Ginneken, B. V., Nayar, S. K., & Koenderink Nayar, J. J. (1999). Reflectance and texture of real-world surfaces. ACM Transactions on Graphics (TOG), 18(1), 1–34.
Caputo, B., Hayman, E., Fritz, M., & Eklundh, J.-O. (2010). Classifying materials in the real world. Image and Vision Computing, 28, 150–163.
Zhang, J., Marszalek, M., Lazebnik, S., & Schmid, C. (2007). Local features and kernels for classication of texture and object categories: A comprehensive study. International Journal of Computer Vision, 73(2), 213–238.
Mahmood, A., Bennamoun, M., An, S., Sohely, F., Boussaid, F., Hovey, R., Kendrick, G., & Fisher, R. B. (2016). Coral classification with hybrid feature representations. In IEEE international conference on image processing (ICIP).
Murala, S., Maheshwari, R. P., & Balasubramanian, R. (2012). Local tetra patterns: A new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing, 21(5), 2874–2886.
Satpathy, A., Jiang, X., & Eng, H.-L. (2014). LBP-based edge-texture features for object recognition. IEEE Transactions on Image Processing, 23(5), 1953–1964.
Zhang, B., Gao, Y., Zhao, S., & Liu, J. (2010). Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing, 19(2), 533–544.
Shakoor, M. H., & Boostani, R. (2017). A novel advanced local binary pattern for image-based coral reef classification. Multimedia Tools and Applications. doi:10.1007/s11042-017-4394-6.
Acknowledgements
The authors would like to thank Oscar Beijbom for making MLC 2012 dataset publicly available on the web, ASM Shihavuddin for providing datasets such as EILAT, EILAT2, RSMAS, LAVA, KTH-Tips and UIUCTEX datasets and J.K.Patterson Edward for providing Suganthi Devadason Marine Research Institute (SDMRI) dataset.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
To calculate the feature extraction using the proposed Z⊕TZLBP, consider a 3 \(\times\) 3 Block \(B_{k}\) in a coral image as shown below:
-
Step 1 The Z neighbors are identified using Eqs. (15–18). Here, the Z neighbors are represented in blue color. Here, the centre pixel is treated as one of the neighbors.
$$D_{1} = 5 - 1 = 4$$$$D_{2} = 1 - 8 = - 7$$$$D_{3} = 8 - 2 = 6$$$$D_{4} = 2 - 3 = - 1$$ -
Step 2 The resultant of LBP for the Z group is given in Eq. (19).
$$LBP\_Z = \left\langle {4, - 7, 6, - 1} \right\rangle$$ -
Step 3 The directional code for the Z neighbors is given by Eq. (20).
$$Directional Code DC\_Z = \left\{ {1, 0, 1, 0} \right\}$$ -
Step 4 The directional codes of Z neighbors are converted into decimal values, the decimal values are summed together to form a feature vector, and is given by Eq. (21).
$$1 \times 2^{0} = 1$$$$1 \times 2^{2} = 4$$$$DEC\_DC\_Z = 5$$ -
Step 5 The TZ neighbors are identified using Eqs. (22)–(25). Here, the TZ neighbors are represented in green color.
$$D_{1}^{{\prime }} = 9 - 11 = - 2$$$$D_{2}^{{\prime }} = 11 - 8 = 3$$$$D_{3}^{{\prime }} = 8 - 7 = 1$$$$D_{4}^{'} = 7 - 4 = 3$$ -
Step 6 The resultant of LBP for the TZ group is given in Eq. (26).
$$LBP_{Z} = \left\langle { - 2, 3, 1, 3} \right\rangle$$ -
Step 7 The directional code for the TZ neighbors is given by Eq. (27).
$$Directional\,Code DC\_Z = \left\{ {0, 1, 1, 1} \right\}$$ -
Step 8 The directional codes of TZ neighbors are converted into decimal values, the decimal values are summed together to form a feature vector, which is given by Eq. (28).
$$1 \times 2^{1} = 2$$$$1 \times 2^{2} = 4$$$$1 \times 2^{3} = 8$$$$DEC\_DC\_Z = 14$$ -
Step 9 Feature Vector FV for a Block \(B_{k}\) is given by Eq. (29).
$${\text{Feature}}\,{\text{Vector}}\,{\text{FV}} = \left\langle {5,14} \right\rangle$$
Rights and permissions
About this article
Cite this article
Ani Brown Mary, N., Dejey, D. Classification of Coral Reef Submarine Images and Videos Using a Novel Z with Tilted Z Local Binary Pattern (Z⊕TZLBP). Wireless Pers Commun 98, 2427–2459 (2018). https://doi.org/10.1007/s11277-017-4981-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-017-4981-x