Attributed Relational SIFT-Based Regions Graph: Concepts and Applications

In the real world, structured data are increasingly represented by graphs. In general, the applications concern the most varied fields, and the data need to be represented in terms of local and spatial connections. In this scenario, the goal is to provide a structure for the representation of a digital image, called the Attributed Relational SIFT-based Regions Graph (ARSRG), previously introduced. ARSRG has not been described in detail, and for this purpose, it is important to explore unknown aspects. In this regard, the goal is twofold: first, to provide a basic theory, which presents formal definitions, not yet specified above, clarifying its structural configuration; second, experimental, which provides key elements about adaptability and flexibility to different applications. The combination of the theoretical and experimental vision highlights how the ARSRG is adaptable to the representation of the images including various contents.

[1]  Yiannis S. Boutalis,et al.  CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval , 2008, ICVS.

[2]  Alfredo Petrosino,et al.  Asymmetric Kernel Scaling for Imbalanced Data Classification , 2011, WILF.

[3]  Jintao Zhang,et al.  An efficient graph-mining method for complicated and noisy data with real-world applications , 2011, Knowledge and Information Systems.

[4]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[5]  Jean Ponce,et al.  A graph-matching kernel for object categorization , 2011, 2011 International Conference on Computer Vision.

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

[7]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[8]  Jeffrey N. Rouder,et al.  A Structural Account of Global and Local Processing , 1999, Cognitive Psychology.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Annette Morales-González,et al.  Simple object recognition based on spatial relations and visual features represented using irregular pyramids , 2013, Multimedia Tools and Applications.

[12]  Effrosini Kokiopoulou,et al.  Mobile Museum Guide Based on Fast SIFT Recognition , 2008, Adaptive Multimedia Retrieval.

[13]  Minsu Cho,et al.  Reweighted Random Walks for Graph Matching , 2010, ECCV.

[14]  Mateja Culjak,et al.  Classification of art paintings by genre , 2011, 2011 Proceedings of the 34th International Convention MIPRO.

[15]  Takumi Kobayashi,et al.  Logistic label propagation , 2012, Pattern Recognit. Lett..

[16]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[17]  José Francisco Martínez Trinidad,et al.  Full duplicate candidate pruning for frequent connected subgraph mining , 2010, Integr. Comput. Aided Eng..

[18]  Arnold W. M. Smeulders,et al.  The Amsterdam Library of Object Images , 2004, International Journal of Computer Vision.

[19]  Mathias Lux,et al.  Lire: lucene image retrieval: an extensible java CBIR library , 2008, ACM Multimedia.

[20]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[21]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Holger Breithaupt,et al.  A day at the museum , 2002 .

[23]  Francesc Serratosa,et al.  Attributed Graph Matching for Image-Features Association Using SIFT Descriptors , 2010, SSPR/SPR.

[24]  Bernt Schiele,et al.  Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[25]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[26]  Jean Ponce,et al.  A Tensor-Based Algorithm for High-Order Graph Matching , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Alfredo Petrosino,et al.  Adjusted F-measure and kernel scaling for imbalanced data learning , 2014, Inf. Sci..

[28]  Francesc Serratosa,et al.  Graph Matching using SIFT Descriptors - An Application to Pose Recovery of a Mobile Robot , 2010, VISAPP.

[29]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[30]  Stepán Obdrzálek,et al.  Object Recognition using Local Affine Frames on Distinguished Regions , 2002, BMVC.

[31]  Peter Vamplew,et al.  The Ballarat Incremental Knowledge Engine , 2010, PKAW.

[32]  Yasuo Ariki,et al.  Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[33]  Raphaël Marée,et al.  Decision Trees and Random Subwindows for Object Recognition , 2005 .

[34]  José Eladio Medina-Pagola,et al.  Frequent approximate subgraphs as features for graph-based image classification , 2012, Knowl. Based Syst..

[35]  Alessandro Rozza,et al.  A Novel Graph Embedding Framework for Object Recognition , 2014, ECCV Workshops.

[36]  Ying Liu,et al.  Region-Based Image Retrieval with Perceptual Colors , 2004, PCM.

[37]  Nobuyuki Morioka Learning Object Representations Using Sequential Patterns , 2008, Australasian Conference on Artificial Intelligence.

[38]  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).

[39]  Alfredo Petrosino,et al.  Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval , 2013, ICIAP.

[40]  Miguel Cazorla,et al.  Topological SLAM Using Omnidirectional Images: Merging Feature Detectors and Graph-Matching , 2010, ACIVS.

[41]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[43]  Francesc Serratosa,et al.  A Discrete Labelling Approach to Attributed Graph Matching Using SIFT Features , 2010, 2010 20th International Conference on Pattern Recognition.

[44]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[45]  Yong Wang,et al.  Tensor Discriminant Analysis for View-based Object Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[46]  Mario Manzo,et al.  Bag of ARSRG Words (BoAW) , 2019, Mach. Learn. Knowl. Extr..

[47]  Miao Qi,et al.  A New Method for Cartridge Case Image Mosaic , 2011, J. Softw..

[48]  Ying Liu,et al.  A survey of content-based image retrieval with high-level semantics , 2007, Pattern Recognit..

[49]  Horst Bischof,et al.  Incremental LDA Learning by Combining Reconstructive and Discriminative Approaches , 2007, BMVC.

[50]  Minsu Cho,et al.  Hyper-graph matching via reweighted random walks , 2011, CVPR 2011.

[51]  Tetsuya Takiguchi,et al.  Generic object recognition by graph structural expression , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[52]  Shih-Fu Chang,et al.  Overview of the MPEG-7 standard , 2001, IEEE Trans. Circuits Syst. Video Technol..

[53]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[54]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[55]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.

[56]  Yiannis S. Boutalis,et al.  FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[57]  A. Trémeau,et al.  Regions adjacency graph applied to color image segmentation , 2000, IEEE Trans. Image Process..

[58]  Minsu Cho,et al.  Progressive graph matching: Making a move of graphs via probabilistic voting , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[59]  Edwin R. Hancock,et al.  3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features , 2008, SSPR/SPR.

[60]  José Eladio Medina-Pagola,et al.  A new proposal for graph-based image classification using frequent approximate subgraphs , 2014, Pattern Recognit..

[61]  Mario Manzo KGEARSRG: Kernel Graph Embedding on Attributed Relational SIFT-Based Regions Graph , 2019, Mach. Learn. Knowl. Extr..