Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.

[1]  Aun Irtaza,et al.  Fusion of local and global features for effective image extraction , 2017, Applied Intelligence.

[2]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[3]  Xiao-Qian Chen,et al.  Classification of farmland images based on color features , 2015, J. Vis. Commun. Image Represent..

[4]  Jing-Yu Yang,et al.  Content-based image retrieval using color difference histogram , 2013, Pattern Recognit..

[5]  Emir Sokic,et al.  Phase preserving Fourier descriptor for shape-based image retrieval , 2016, Signal Process. Image Commun..

[6]  M. D. Ingole,et al.  Content based image retrieval using hybrid features and various distance metric , 2018, Journal of Electrical Systems and Information Technology.

[7]  Paul Townend,et al.  Improving content-based image retrieval for heterogeneous datasets using histogram-based descriptors , 2017, Multimedia Tools and Applications.

[8]  Prashant Srivastava,et al.  Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval , 2017, J. Vis. Commun. Image Represent..

[9]  Senlin Yan,et al.  Computing invariants of Tchebichef moments for shape based image retrieval , 2016, Neurocomputing.

[10]  Aisha Hassan Abdalla Hashim,et al.  A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications , 2020, Wireless Personal Communications.

[11]  Sarosh H. Patel,et al.  Predicting Emerging Trends on Social Media by Modeling it as Temporal Bipartite Networks , 2020, IEEE Access.

[12]  Prabir Kumar Biswas,et al.  Rotation and scale invariant texture features using discrete wavelet packet transform , 2003, Pattern Recognit. Lett..

[13]  Urbano Nunes,et al.  Trainable classifier-fusion schemes: An application to pedestrian detection , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[14]  R. Krishnamoorthi,et al.  A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model , 2012, J. Vis. Commun. Image Represent..

[15]  Partha Pratim Roy,et al.  A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern , 2018, Pattern Analysis and Applications.

[16]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[17]  Vikrant Bhateja,et al.  Information Systems Design and Intelligent Applications , 2019, Advances in Intelligent Systems and Computing.

[18]  Yogita D. Mistry,et al.  Textural and color descriptor fusion for efficient content-based image retrieval algorithm , 2020, Iran J. Comput. Sci..

[19]  Xiangyang Wang,et al.  Robust image retrieval based on color histogram of local feature regions , 2010, Multimedia Tools and Applications.

[20]  Jing Huang,et al.  Spatial Color Indexing and Applications , 2004, International Journal of Computer Vision.

[21]  S. G. Shaila,et al.  Indexing and encoding based image feature representation with bin overlapped similarity measure for CBIR applications , 2016, J. Vis. Commun. Image Represent..

[22]  Rosilah Hassan,et al.  An Implementation Study of DMM PMIPV6 Protocol on Dual-Stack Network Environment , 2018 .

[23]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[24]  Min Huang,et al.  Content-based image retrieval technology using multi-feature fusion , 2015 .

[25]  Yan Zhou,et al.  Hierarchical Visual Perception and Two-Dimensional Compressive Sensing for Effective Content-Based Color Image Retrieval , 2016, Cognitive Computation.

[26]  Rehan Ashraf,et al.  Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions , 2015, Entropy.

[27]  Anne E. James,et al.  Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics , 2012, J. Comput. Syst. Sci..

[28]  James Ze Wang,et al.  Real-Time Computerized Annotation of Pictures , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Puteh Saad,et al.  Object Detection using Geometric Invariant Moment , 2006 .

[30]  M. Kamarasan,et al.  Statistical framework for image retrieval based on multiresolution features and similarity method , 2013, Multimedia Tools and Applications.

[31]  Rosilah Hassan,et al.  Delay aware Reactive Routing Protocols for QoS in MANETs: a Review , 2013 .

[32]  Yassine Ruichek,et al.  Efficient Combination of Color, Texture and Shape Descriptor, Using SLIC Segmentation for Image Retrieval , 2017 .

[33]  Aman Pal,et al.  Fusion framework for effective color image retrieval , 2014, J. Vis. Commun. Image Represent..

[34]  Haider Banka,et al.  A prominent object region detection based approach for CBIR application , 2016, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC).

[35]  Tae-Sun Choi,et al.  Embedding neural networks for semantic association in content based image retrieval , 2014, Multimedia Tools and Applications.

[36]  Raed A. Alsaqour,et al.  Using Dendritic Cell Algorithm to Detect the Resource Consumption Attack over MANET , 2011, ICSECS.

[37]  Xiangyang Wang,et al.  An effective image retrieval scheme using color, texture and shape features , 2011, Comput. Stand. Interfaces.

[38]  Saeid Belkasim,et al.  Farthest point distance: A new shape signature for Fourier descriptors , 2009, Signal Process. Image Commun..

[39]  Jens Krommweh,et al.  Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation , 2010, J. Vis. Commun. Image Represent..

[40]  Demetrio Labate,et al.  Optimally Sparse Multidimensional Representation Using Shearlets , 2007, SIAM J. Math. Anal..

[41]  Ram Bilas Pachori,et al.  Histogram refinement for texture descriptor based image retrieval , 2017, Signal Process. Image Commun..

[42]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[43]  Phil Sallee,et al.  Current challenges in automating visual perception , 2008, 2008 37th IEEE Applied Imagery Pattern Recognition Workshop.

[44]  Debi Prosad Dogra,et al.  Retrieval of colour and texture images using local directional peak valley binary pattern , 2020, Pattern Analysis and Applications.

[45]  Haibing Wang,et al.  Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models , 2016, Neurocomputing.

[46]  Vipin Tyagi,et al.  Region Based Image Retrieval Using Integrated Color, Texture and Shape Features , 2015 .

[47]  Chi Chung Ko,et al.  Using moment invariants and HMM in facial expression recognition , 2002, Pattern Recognit. Lett..

[48]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[49]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[50]  Mutasem K. Alsmadi,et al.  Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features , 2020, Arabian Journal for Science and Engineering.

[51]  Xing-Yuan Wang,et al.  An effective method for color image retrieval based on texture , 2012, Comput. Stand. Interfaces.

[52]  Jing-Yu Yang,et al.  Content-based image retrieval using computational visual attention model , 2015, Pattern Recognit..

[53]  Wen-Yuan Chen,et al.  Hand gesture recognition using valley circle feature and Hu’s moments technique for robot movement control , 2016 .

[54]  Guihua Wen,et al.  Weighted spectral features based on local Hu moments for speech emotion recognition , 2015, Biomed. Signal Process. Control..

[55]  Sarosh H. Patel,et al.  Partial Observer Decision Process Model for Crane-Robot Action , 2020, Sci. Program..

[56]  Vipin Tyagi,et al.  Texture image retrieval using adaptive tetrolet transforms , 2016, Digit. Signal Process..

[57]  Takio Kurita,et al.  Selection of Histograms of Oriented Gradients Features for Pedestrian Detection , 2007, ICONIP.

[58]  Oscar Déniz-Suárez,et al.  Face recognition using Histograms of Oriented Gradients , 2011, Pattern Recognit. Lett..

[59]  Mohsen Ebrahimi Moghaddam,et al.  A content-based image retrieval system based on Color Ton Distribution descriptors , 2013, Signal, Image and Video Processing.

[60]  Othman Omran Khalifa,et al.  Design and Evaluation of a Multihoming-Based Mobility Management Scheme to Support Inter Technology Handoff in PNEMO , 2020, Wireless Personal Communications.

[61]  Rassoul Amirfattahi,et al.  A New Content-Based Image Retrieval System Based on Optimized Integration of DCD , Wavelet and Curvelet Features , 2016 .

[62]  Jing-Ming Guo,et al.  Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding , 2015, IEEE Transactions on Image Processing.

[63]  Guowei Yang,et al.  Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix , 2016, Neurocomputing.

[64]  Soontorn Oraintara,et al.  Statistical texture retrieval in noise using complex wavelets , 2013, Signal Process. Image Commun..