A Study on Image Retrieval Based on Tetrolet Transform

  • Authors

    • Devika Sarath
    • M Sucharitha
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17964
  • Image retrieval, tetrolettransform, Texture image retrieval, Content based retreival

  • Abstract

    Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying tertrolet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.

  • References

    1. [1] Candès EJ, Donoho DL, “New tight frames of curvelets and optimal representations of objects with piecewise C2 singularitiesâ€, Commun. Pure Appl. Math., Vol.57, No.2, (2004), pp.219–266.

      [2] Vetterli M, “The contourlet transform: an efficient directional multiresolution image representationâ€, IEEE Trans. Image Process., Vol.14, No.12, (2005), pp.2091–2106.

      [3] Velisavljevic V, Beferull-Lozano, B, Vetterli, M & Dragotti, PL, “Directionlets: anisotropic multi-directional representation with separable filteringâ€, IEEE Trans. Image Process., Vol.17, No.7, (2006), pp.1916–1933.

      [4] Reddy AH & Chandra NS, “Local oppugnantcolor space extrema patterns for content based natural and texture image retrievalâ€, Int. J. Electron. Commun., (AEÃœ), Vol.69, No.1, (2014), pp.290–298.

      [5] Vision MIT, Modeling Group, Vision Texture, (1995).

      [6] Raghuwanshi G & Tyagi V, “Texture image retrieval using adaptive tetrolet transformsâ€, Digital Signal Processing, Vol.48, (2016), pp.50-57.[7] Jens K, “Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representationâ€, Journal of Visual. Communication and Image Representation.Vol.21, (2010), pp.364–374.[8] Shi C, Zhang J, Chen H & Zhang Y, “A novel hybrid method for remote sensing image approximation using the tetrolet transformâ€, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.7, No.12, (2014), pp.4949-4959.[9] Niteesh B, Ravi Kumar V, Sai Ram R, KrishnaSagar P & HemanthNag B, “Image Compression Using Adaptive Haar Wavelet Based TetroletTransformâ€, International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, No.4, (2016).[10] Hui-Xian Y & Yong-Yong C, “Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illuminationâ€, IET Biometrics, Vol.5, No.2, (2016), pp.76-82.[11] Abdelouahad AA, Omari M, Cherifi H, Alibouch B & El Hassouni M, “Tetrolet-based reduced reference image quality assessment approachâ€, IEEE International Conference on Multimedia Computing and Systems (ICMCS), (2014), pp.52-56.

      [12] Wang L, Xiao L, Zhang J & Wei Z, “New image restoration method associated with tetrolets shrinkage and weighted anisotropic total variationâ€, Signal processing, Vol.93, No.4, (2013), pp.661-670.

      [13] Zhou X & Wang W, “Infrared and visible image fusion based on Tetrolet transformâ€, Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, (2016), pp.701-708.

      [14] Ceylan M & Canbilen AE, “Performance Comparison of Tetrolet Transform and Wavelet-Based Transforms for Medical Image Denoisingâ€, International Journal of Intelligent Systems and Applications in Engineering, Vol.5, No.4, (2017), pp.222-231.

      [15] Chun YD, Kim NC & Jang IH, “Content-based image retrieval using multiresolution color and texture featuresâ€, IEEE Transactions on Multimedia, Vol.10, No.6, (2008), pp.1073-1084.[16] Vasimbabu MD, Subhasri BA & Hemahowdary EM, “An efficient magnetic resonance brain image classifier using tetrolet transform and kernel support vector machine based on OTSU binarizationâ€, International Journal of Engineering & Technology, Vol.7, (2018), pp.111-115.[17] Singh RD, “Tetrolet Transform Based Satellite Image Enhancementâ€, Journal of Engineering and Applied Sciences, Vol.12, No.15, (2017), pp.3930-3932.[18] Ramasamy R, Vidhya T & Siyamala Devi M, “Performance comparsion of image denoising in natural images susingtetrolet transform with shrinkage techniquesâ€, International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), Vol.12, No.4, (2015).[19] Jain P & Tyagi V, “An adaptive edge-preserving image denoising using epsilon-median filtering in tetrolet domainâ€, Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India (CSI), (2015), pp.393-400.[20] Thayammal S & Selvathi D. , February). Multispectral band image compression using adaptive wavelet transform-Tetrolet transform. IEEE International Conference on Electronics and Communication Systems (ICECS), (2014), pp. 1-5.

      [21] Singh MK, Denoising of natural images using the wavelet transform, M.S. thesis, San Jose State University, (2010).

      [22] Jain P & Tyagi V, “An adaptive edge-preserving image denoising technique using tetrolettransformsâ€, Visual Computer, Vol.31, (2014), pp.657-67

      [23] Ceylan M & Ozturk AE, “Determining the number of tetrominoe orders for denoising applications performed by tetrolet transformâ€, 22nd IEEE Conference on Signal Processing and Communications Applications (SIU), (2014), 216-219.

      [24] Mohan MM & Sheeba VS, “A novel method of medical image denoising using bilateral and NLM filteringâ€, IEEE Third International Conference on Advances in Computing and Communications (ICACC), (2013), 186-191.

      [25] Breukelaar R, Demaine E, Hohenberger S, Hoogeboom H, Kosters W & Liben-Nowell D, “Tetris is hard, even to approximateâ€, Int. J. Comput. Geom. Appl., Vol.14, No.1–2, (2004), pp.41–68.

      [26] Chang CL & Girod B, “Direction-adaptive discrete wavelet transform for image compressionâ€, IEEE Transactions on Image Processing, Vol.16, No.5, (2007), pp.1289-1302.

      [27] Ding W, Wu F, Wu X, Li S & Li H, “Adaptive directional lifting-based wavelet transform for image codingâ€, IEEE Trans. Image Process., Vol.16, No.2, (2007), pp.416–427.

      [28] Donoho, DL, “Wedgelets: nearly minimax estimation of edgesâ€, Ann. Statist., Vol.27, No.3 (1999), pp.859–897.

      [29] Velisavljevic V, Beferull-Lozano B, Vetterli M & Dragotti PL, “Directionlets:anisotropic multi-directional representation with separable filteringâ€, IEEE Trans. Image Process., Vol.17, No.7, (2006), pp.1916–1933.

      [30] Do MN & Vetterli M, “The contourlet transform: an efficient directional multiresolution image representationâ€, IEEE Trans. Image Process., Vol.14, No.12, (2005), pp.2091-2106.

      [31] Chang CL & Girod B, “Direction-adaptive discrete wavelet transform for image compressionâ€, IEEE Trans. Image Process., Vol.16, No.5, (2007), pp.1289–1302.

      [32] Velisavljevic V, Beferull-Lozano B, Vetterli M & Dragotti PL, “Directionlets: Anisotropic multidirectional representation with separable filteringâ€, IEEE Transactions on Image Processing, Vol.15, No.7, (2006), pp.1916- 1933.

      [33] Guo K & Labate D, “Optimally sparse multi dimensional representation using shearletsâ€, SIAM Journal on Mathematical Analysis, Vol.39, No.1, (2007), pp.298-318.

      [34] Wang Z, Bovik A, Sheikh H & Simoncelli E, “Image quality assessment: from error visibility to structural similarityâ€, IEEE Transactions on Image Processing, Vol.13, No.4, (2004), pp.600–612.

      [35] Gonzalez RC & Woods RE, Digital image processing, Prentice-Hall, Upper Saddle River, (2008).

      [36] Do MN & Vetterli M, “Wavelet-based texture retrieval using generalized gaussian density and Kullback–Leibler distanceâ€, IEEE Trans. Image Process., Vol.11, No.2, (2002), pp.146–158.

      [37] A Akhmetbekova, P Auyesbayeva, Sh Ibrayev (2018). Turkic "Hikaya" genre and its characters. Opción, Año 33. 81-106.

      [38] A Mukanbetkaliyev, S Amandykova, Y Zhambayev, Z Duskaziyeva, A Alimbetova (2018). The aspects of legal regulation on staffing of procuratorial authorities of the Russian Federation and the Republic of Kazakhstan Opción, Año 33. 187-216.

  • Downloads

  • How to Cite

    Sarath, D., & Sucharitha, M. (2018). A Study on Image Retrieval Based on Tetrolet Transform. International Journal of Engineering & Technology, 7(3.27), 321-324. https://doi.org/10.14419/ijet.v7i3.27.17964

    Received date: 2018-08-20

    Accepted date: 2018-08-20

    Published date: 2018-08-15