Introducing Global and Local Walsh Wavelet Transform for Texture Pattern Based Image Retrieval

Summary The theme of the work presented in the paper is novel texture pattern based image retrieval techniques using ternary image maps and two different non-sinusoidal orthogonal Walsh wavelet transforms namely Global Walsh Wavelet Transform and Local Walsh Wavelet Transform. Different texture patterns namely ‘64-pattern’ and ‘256-pattern’ are generated using both the Walsh wavelet transform matrices giving rise to global and local texture patterns. The generated texture patterns are then compared with the ternary image maps to generate the feature vector based on structural matching as the matching number of ones, zeros, minus ones per Walsh wavelet texture pattern. Here total 4 variations of the proposed novel image retrieval methods using texture patterns are considered with two different texture patterns and two different ways to generate these texture patterns (Global and Local). The proposed texture content based image retrieval (CBIR) techniques are tested on the image database with help of 55 queries (randomly selected 5 from each of 11 image categories) fired on image database. The performance comparison of texture pattern based CBIR methods is done with help of precision-recall crossover points.

[1]  Sudeep D. Thepade,et al.  Amelioration of Walsh-Hadamard Texture Patterns based Image Retrieval using HSV Color Space , 2011 .

[2]  Sudeep D. Thepade,et al.  Performance Comparison of Texture Pattern Based Image Retrieval Methods using Walsh, Haar and Kekre Transforms with Assorted Thresholding , 2011 .

[3]  Sudeep D. Thepade,et al.  Query by image texture pattern content using Haar transform matrix and image bitmaps , 2011, ICWET.

[4]  Sudeep D. Thepade,et al.  Improving Performance of Multileveled BTC Based CBIR Using Sundry Color Spaces , 2011 .

[5]  Sudeep D. Thepade,et al.  Performance Comparison of Texture Pattern based Image Retrieval using Haar Transform with Binary and Ternary Image Maps , 2011 .

[6]  Sudeep D. Thepade,et al.  Performance Comparison of Gradient Mask Texture Based Image Retrieval Techniques using Walsh, Haar and Kekre Transforms with Image Maps , 2011 .

[7]  Sudeep D. Thepade,et al.  Performance evaluation of image retrieval using energy compaction and imagetiling over DCT row mean and DCT column mean , 2011 .

[8]  Sudeep D. Thepade,et al.  Improved texture feature based image retrieval using Kekre’s fast codebook generation algorithm , 2011 .

[9]  Sudeep D. Thepade,et al.  Image Retrieval Using Texture Patterns Generated from Walsh-Hadamard Transform Matrix and Image Bitmaps , 2011 .

[10]  Dr. H. B. Kekre.  Performance Comparison of Texture Pattern based Image Retrieval using Haar Transform with Binary and Ternary Image Maps , 2011 .

[11]  Sudeep D. Thepade,et al.  Image retrieval by Kekre's transform applied on each row of Walsh transformed VQ codebook , 2010, ICWET.

[12]  Akshay Maloo,et al.  Query by Image Content Using Colour Averaging Techniques , 2010 .

[13]  Sudeep D. Thepade,et al.  Amelioration of Colour Averaging Based Image Retrieval Techniques using Even and Odd parts of Images , 2010 .

[14]  Sudeep D. Thepade,et al.  Walsh Transform over Row Mean and Column Mean using Image Fragmentation and Energy Compaction for Image Retrieval , 2010 .

[15]  Sudeep D. Thepade,et al.  Performance Comparison of Image Retrieval Using Fractional Coefficients of Transformed Image Using DCT , Walsh , Haar and Kekre ’ s Transform , 2010 .

[16]  Sudeep D. Thepade,et al.  Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook , 2009 .

[17]  Sudeep D. Thepade,et al.  Scaling Invariant Fusion of Image Pieces In Panorama Making And Novel Image Blending Technique , 2009 .

[18]  Sudeep D. Thepade,et al.  Improving `Color to Gray and Back' using Kekre's LUV Color Space , 2009, 2009 IEEE International Advance Computing Conference.

[19]  Sudeep D. Thepade,et al.  Image retrieval using augmented block truncation coding techniques , 2009, ICAC3 '09.

[20]  Antonietta Gatti,et al.  Color Based Image Retrieval using Amendment of Block Truncation Coding with YCbCr Color Space , 2009 .

[21]  Sudeep D. Thepade,et al.  Color Traits Transfer to Grayscale Images , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[22]  Sudeep D. Thepade,et al.  Creating the Color Panoramic View using Medley of Grayscale and Color Partial Images , 2008 .

[23]  Enhanced fast encoding method for vector quantization by finding an optimally-ordered Walsh transform kernel , 2005, IEEE International Conference on Image Processing 2005.

[24]  Kanad K. Biswas,et al.  Region-based image retrieval using integrated color, shape, and location index , 2004, Comput. Vis. Image Underst..

[25]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[26]  Toshikazu Kato,et al.  Query by Visual Example - Content based Image Retrieval , 1992, EDBT.

[27]  Sudeep D. Thepade,et al.  Boosting Block Truncation Coding with Kekre ’ s LUV Color Space for Image Retrieval , 2022 .