Local structure co-occurrence pattern for image retrieval

Abstract. Image description and annotation is an active research topic in content-based image retrieval. How to utilize human visual perception is a key approach to intelligent image feature extraction and representation. This paper has proposed an image feature descriptor called the local structure co-occurrence pattern (LSCP). LSCP extracts the whole visual perception for an image by building a local binary structure, and it is represented by a color-shape co-occurrence matrix which explores the relationship of multivisual feature spaces according to visual attention mechanism. As a result, LSCP not only describes low-level visual features integrated with texture feature, color feature, and shape feature but also bridges high-level semantic comprehension. Extensive experimental results on an image retrieval task on the benchmark datasets, corel-10,000, MIT VisTex, and INRIA Holidays, have demonstrated the usefulness, effectiveness, and robustness of the proposed LSCP.

[1]  Jeremy M. Wolfe,et al.  The Level of Attention: Mediating Between the Stimulus and Perception , 2003 .

[2]  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.

[3]  Ken Nakayama,et al.  Attentional requirements in a ‘preattentive’ feature search task , 1997, Nature.

[4]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[5]  Jianzhong Wang,et al.  A novel image retrieval method based on hybrid information descriptors , 2014, J. Vis. Commun. Image Represent..

[6]  B. Julesz.  Textons, the elements of texture perception, and their interactions , 1981, Nature.

[7]  George Economou,et al.  A generic scheme for color image retrieval based on the multivariate Wald-Wolfowitz test , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Matti Pietikäinen,et al.  Texture Classification using a Linear Configuration Model based Descriptor , 2011, BMVC.

[9]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[10]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[11]  Matti Pietikäinen,et al.  Discriminative features for texture description , 2012, Pattern Recognit..

[12]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[13]  Andrew Zisserman,et al.  All About VLAD , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Zhe L. Lin,et al.  A Local Bag-of-Features Model for Large-Scale Object Retrieval , 2010, ECCV.

[15]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[16]  Thierry Pun,et al.  Performance evaluation in content-based image retrieval: overview and proposals , 2001, Pattern Recognit. Lett..

[17]  Cordelia Schmid,et al.  Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Scott B. Steinman,et al.  Computational Models of Visual Attention , 2002 .

[19]  Gustavo Carneiro,et al.  Supervised Learning of Semantic Classes for Image Annotation and Retrieval , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[21]  Wen-Liang Hwang,et al.  An Examplar-Based Approach for Texture Compaction Synthesis and Retrieval , 2010, IEEE Transactions on Image Processing.

[22]  Subrahmanyam Murala,et al.  Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval , 2012, IEEE Transactions on Image Processing.

[23]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  Joachim M. Buhmann,et al.  Empirical evaluation of dissimilarity measures for color and texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[25]  Lei Zhang,et al.  Image retrieval based on micro-structure descriptor , 2011, Pattern Recognit..

[26]  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).

[27]  C. Schmid,et al.  Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search - extended version , 2008 .

[28]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Changhu Wang,et al.  Spatial-bag-of-features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  Campbell Wilson,et al.  Feature Re-weighting in Content-Based Image Retrieval , 2006, CIVR.

[32]  J Theeuwes,et al.  Visual selective attention: a theoretical analysis. , 1993, Acta psychologica.

[33]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..

[34]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Matti Pietikäinen,et al.  Rotation-invariant texture classification using feature distributions , 2000, Pattern Recognit..

[36]  Md. Monirul Islam,et al.  A review on automatic image annotation techniques , 2012, Pattern Recognit..

[37]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[38]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[39]  Liming Chen,et al.  Image region description using orthogonal combination of local binary patterns enhanced with color information , 2013, Pattern Recognit..

[40]  Qiang Ji,et al.  Texture analysis for classification of cervix lesions , 2000, IEEE Transactions on Medical Imaging.

[41]  Alice Porebski,et al.  Color texture analysis using CFA chromatic co-occurrence matrices , 2013, Comput. Vis. Image Underst..

[42]  Xingyuan Wang,et al.  A novel method for image retrieval based on structure elements' descriptor , 2013, J. Vis. Commun. Image Represent..

[43]  Nick Cercone,et al.  Local Triplet Pattern for Content-Based Image Retrieval , 2009, ICIAR.