A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern

Abstract This paper presents a new methodology to retrieve images of different scenes by introducing a novel image descriptor. The proposed descriptor works with Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and any other feature descriptor that can be applied on the image pixels. As the proposed descriptor considers a group of pixels together, higher level of semantic is achieved. In this work, a new image descriptor using SIFT and LDP is introduced that is able to find similarities and matches between images. The proposed descriptor produces highly discriminative features for describing image content. Four image datasets are used for evaluating our proposed descriptor. Comprehensive experiments have been conducted using various classifiers and different image features to show the superiority of the proposed method.

[1]  Antanas Verikas,et al.  Increasing the discrimination power of the co-occurrence matrix-based features , 2007, Pattern Recognit..

[2]  Laurent Itti,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Rapid Biologically-inspired Scene Classification Using Features Shared with Visual Attention , 2022 .

[3]  Gholam Ali Montazer,et al.  An improved radial basis function neural network for object image retrieval , 2015, Neurocomputing.

[4]  Juan Humberto Sossa Azuela,et al.  Self organizing natural scene image retrieval , 2013, Expert Syst. Appl..

[5]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[6]  Nelson H. C. Yung,et al.  Feature fusion within local region using localized maximum-margin learning for scene categorization , 2012, Pattern Recognit..

[7]  Po-Whei Huang,et al.  Image retrieval by texture similarity , 2003, Pattern Recognit..

[8]  Xi Zhang,et al.  Feature integration analysis of bag-of-features model for image retrieval , 2013, Neurocomputing.

[9]  Matti Pietikäinen,et al.  Block-Based Methods for Image Retrieval Using Local Binary Patterns , 2005, SCIA.

[10]  Hermann Ney,et al.  Features for image retrieval: an experimental comparison , 2008, Information Retrieval.

[11]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[12]  Dewen Hu,et al.  Scene classification using a multi-resolution bag-of-features model , 2013, Pattern Recognit..

[13]  Jean Ponce,et al.  Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Rong-Tai Chen,et al.  A smart content-based image retrieval system based on color and texture feature , 2009, Image Vis. Comput..

[15]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  R. S-A. Gatsaeva,et al.  On the representation of continuous functions of several variables as superpositions of continuous functions of one variable and addition , 2018 .

[17]  Devi Parikh.  Recognizing jumbled images: The role of local and global information in image classification , 2011, 2011 International Conference on Computer Vision.

[18]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Tinne Tuytelaars,et al.  Effective Use of Frequent Itemset Mining for Image Classification , 2012, ECCV.

[20]  ObjectPatchNet: Towards scalable and semantic image annotation and retrieval , 2014, Comput. Vis. Image Underst..

[21]  Tieniu Tan,et al.  Feature Coding in Image Classification: A Comprehensive Study , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[23]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[24]  Sheng-Hsun Hsu,et al.  Application of SVM and ANN for image retrieval , 2006, Eur. J. Oper. Res..

[25]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[26]  M. Esmel ElAlami,et al.  A novel image retrieval model based on the most relevant features , 2011, Knowl. Based Syst..

[27]  N. H. C. Yung,et al.  Scene categorization via contextual visual words , 2010, Pattern Recognit..

[28]  Thomas Serre,et al.  A New Biologically Inspired Color Image Descriptor , 2012, ECCV.

[29]  Ioannis Pratikakis,et al.  Bag of spatio-visual words for context inference in scene classification , 2013, Pattern Recognit..

[30]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[32]  Sudipta Mukhopadhyay,et al.  Content-based texture image retrieval using fuzzy class membership , 2013, Pattern Recognit. Lett..

[33]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[34]  Larry S. Davis,et al.  Human detection using partial least squares analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[35]  Rui Hu,et al.  A performance evaluation of gradient field HOG descriptor for sketch based image retrieval , 2013, Comput. Vis. Image Underst..

[36]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[37]  Robert J. Gillies,et al.  Texture Feature Analysis to Predict Metastatic and Necrotic Soft Tissue Sarcomas , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[38]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[39]  Zhuowen Tu,et al.  Detecting Object Boundaries Using Low-, Mid-, and High-level Information , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[41]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[42]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[43]  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).

[44]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[45]  M. Esmel ElAlami,et al.  A new matching strategy for content based image retrieval system , 2014, Appl. Soft Comput..

[46]  Natalia Vassilieva.  Content-based image retrieval methods , 2009, Programming and Computer Software.

[47]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[48]  Sang-Sung Park,et al.  Expert system based on artificial neural networks for content-based image retrieval , 2005, Expert Syst. Appl..

[49]  Peter Lambert,et al.  Bridging the Semantic Gap using Human Vision System Inspired Features , 2010 .

[50]  Ayoub Al-Hamadi,et al.  Cubic-splines neural network- based system for Image Retrieval , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[51]  Thomas Mensink,et al.  Image Classification with the Fisher Vector: Theory and Practice , 2013, International Journal of Computer Vision.

[52]  Claudio Gutierrez,et al.  Survey of graph database models , 2008, CSUR.

[53]  Yong Wang,et al.  Conditional Random Field for Natural Scene Categorization , 2007, BMVC.

[54]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.