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Region and texture based effective image extraction

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Abstract

Images can be well defined by the primitive shape and texture features. In this paper we combined these features in a novel way. We presented a scheme of the local interest point detection and their global description in a more meaningful way. We extracted the image signatures by assembling the interest points at different levels of the representation. For this, at first shape feature are collected by grouping the connected pixels to create the regions based on binary intensity threshold. Histogram of oriented gradients is used to describe the features for the detected interest points returned for maximally stable regions. These signatures are combined with rotation invariant texture features extracted by using uniform local binary pattern after applying the proposed reordering algorithm. The algorithm intakes the number of observations for the region and texture features and returns the compact dimensions as input for the limited principal components computation. Our proposed technique is experimented against the existing research methods on Corel-100, Caltech-101 and Caltech-256 datasets and outperforms in many image categories. Experimentation results show that a combination of local and global features strengthens the capability of the proposed method to retrieve the foreground and background objects. Furthermore, feature description using sliding window fashion makes this approach more robust to object recognition.

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References

  1. Dalal, N., and Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1, pp. 886–893. IEEE (2005)

  2. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image. Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  3. Zhao, G., Ahonen, T., Matas, J., Pietikainen, M.: Rotation-invariant image and video description with local binary pattern features. IEEE Trans. Image Process. 21(4), 1465–1477 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  4. Prathap, K.S.V., Jilani, S.A.K., Ramana Reddy, P.: A critical review on Image Mosaicing. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–8. IEEE (2016)

  5. Moravec, H.: Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Technical Report CMU-RI-TR-3, Carnegie-Mellon University, Robotics Institute (1980)

  6. Harris, C., Stephens, M.: A combined corner and edge detector. Alvey Vis. Conf. 15, 50 (1988)

    Google Scholar 

  7. Zhao, W.J., Gong, S.R., Liu, C.P., Shen, X.J.: Adaptive Harris corner detection algorithm. Comput. Eng. 10(5), 212–215 (2008)

    Google Scholar 

  8. Shi, J., Carlo, T.: Good features to track. In: Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94., 1994 IEEE Computer Society Conference on, pp. 593–600. IEEE (1994)

  9. Yu, Y., Huang, K., Tan, T.: A Harris-like scale invariant feature detector. Asian Conference on Computer Vision, pp. 586–595. Springer, Heidelberg (2009)

    Google Scholar 

  10. Winnemöller, H.: XDoG: advanced image stylization with extended difference-of-gaussians.” In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering, pp. 147–156. ACM (2011)

  11. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina key point. In: Computer vision and pattern recognition (CVPR), 2012 IEEE conference on, pp. 510–517. IEEE (2012)

  12. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. European Conference on Computer Vision, pp. 430–443. Springer, Heidelberg (2006)

    Google Scholar 

  13. Lowe, D.G.: Object recognition from local scale-invariant features. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol. 2, pp. 1150–1157. IEEE (1999)

  14. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2013)

    MATH  Google Scholar 

  15. Mehta, N., Prasad, S.V.A.V., Arya, L., Pant. M.: A novel approach for the analysis of US images using morphological image processing techniques. In: Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, pp. 868–872. IEEE, (2015)

  16. Zuo, C., Chen, Q., Sui, X.: Range limited bi-histogram equalization for image contrast enhancement. Optik-Int. J. Light Electron Optics 124(5), 425–431 (2013)

    Article  Google Scholar 

  17. Senthilkumaran, N., Thimmiaraja J.: Histogram equalization for image enhancement using MRI brain images. In: Computing and Communication Technologies (WCCCT), 2014 World Congress on, pp. 80–83. IEEE (2014)

  18. Ryu, S.J., Kirchner, M., Lee, M.J., Lee, H.K.: Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans. Inf. Forensics Secur. 8(8), 1355–1370 (2013)

    Article  Google Scholar 

  19. Chang, T.Y., Tai, S.C., Lin G.S.: A near-duplicate video retrieval method based on Zernike moments. In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 860–864. IEEE (2015)

  20. Hsieh, J.W., Grimson, W.E.L., Chiang, C.C., Huang, Y.S.: Region-based image retrieval. In: Image Processing, 2000. Proceedings. 2000 International Conference on, vol. 1, pp. 77–80. IEEE (2000)

  21. Jing, F., Li, M., Zhang, H.J., Zhang, B.: An efficient and effective region-based image retrieval framework. IEEE Trans. Image Process. 13(5), 699–709 (2004)

    Article  Google Scholar 

  22. Prasad, B.G., Biswas, K.K., Gupta, S.K.: Region-based image retrieval using integrated color, shape, and location index. Comput Vis. Image Underst. 94(1), 193–233 (2004)

    Article  Google Scholar 

  23. Jing, F., Li, M., Zhang, H.J., Zhang, B.: Relevance feedback in region-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 14(5), 672–681 (2004)

    Article  Google Scholar 

  24. Zand, M., Doraisamy, S., Halin, A.A., Mustaffa, M.R.: Texture classification and discrimination for region-based image retrieval. J. Vis. Commun. Image Represent. 26, 305–316 (2015)

    Article  Google Scholar 

  25. Papadopoulos, G.T., Apostolakis, K.C., Daras, P.: Gaze-based relevance feedback for realizing region-based image retrieval. IEEE Trans. Multimed. 16(2), 440–454 (2014)

    Article  Google Scholar 

  26. Shrivastava, N., Tyagi, V.: Region Based Image Retrieval Using Integrated Color, Texture and Shape Features. In: Information systems design and intelligent applications, pp. 309–316. Springer India, (2015)

  27. Krishnamurthy, L., Kesavan, J.: An improved content based image retrieval using three region colour and straight line signatures of the image. In: Communications and Signal Processing (ICCSP), 2015 International Conference on, pp. 1919–1924. IEEE (2015)

  28. Felci Rajam, I., Valli, S.: Region-based image retrieval using the semantic cluster matrix and adaptive learning. Int. J. Comput. Sci. Eng. 7(3), 239–252 (2012)

    Article  Google Scholar 

  29. Iqbal, K., Odetayo, M.O., James, A.: Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics. J. Comput. Syst. Sci. 78(4), 1258–1277 (2012)

    Article  MathSciNet  Google Scholar 

  30. Shen, G.L., Wu, X.J.: Content based image retrieval by combining color, texture and CENTRIST. In: Signal Processing (CIWSP 2013), 2013 Constantinides International Workshop on, pp. 1–4. IET (2013)

  31. Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1075–1088 (2003)

    Article  Google Scholar 

  32. Liu, Guang-Hai, Yang, Jing-Yu.: Content-based image retrieval using color difference histogram. Pattern Recognit. 46(1), 188–198 (2013)

    Article  Google Scholar 

  33. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model Based Vision. (2004)

  34. Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. (2007)

  35. Lin, C.H., Chen, R.T., Chan, Y.K.: A smart content-based image retrieval system based on color and texture feature. Image Vis. Comput. 27, 658–665 (2009)

  36. Yildizer, E., Balci, A.M., Hassan, M., Alhajj, R.: Efficient content-based image retrieval using multiple support vector machines ensemble. Expert Syst. Appl. 39, 2385–2396 (2012)

    Article  Google Scholar 

  37. Rao, M.B., Rao, B.P., Govardhan, A.: CTDCIRS: Content based image retrieval system based on dominant color and texture features. Int. J. Comput. Appl. 18, 40–46 (2011)

    Google Scholar 

  38. Wang, C., Zhang, B., Qin, Z., Xiong, J.:Spatial weighting for bag-of-features based image retrieval. In: Integrated uncertainty in knowledge modelling and decision making, pp. 91–100. Springer, New York (2013)

  39. ElAlami, M.E.: A novel image retrieval model based on the most relevant features. Knowl. Based Syst. 24(1), 23–32 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan

    Khawaja Tehseen Ahmed

  2. Faculty of IT, University of Central Punjab, Lahore, Pakistan

    Muhammad Amjad Iqbal

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  1. Khawaja Tehseen Ahmed
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  2. Muhammad Amjad Iqbal
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Correspondence to Khawaja Tehseen Ahmed.

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Ahmed, K.T., Iqbal, M.A. Region and texture based effective image extraction. Cluster Comput 21, 493–502 (2018). https://doi.org/10.1007/s10586-017-0915-3

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