Content-based image retrieval using visually significant point features

This paper presents a new image retrieval scheme using visually significant point features. The clusters of points around significant curvature regions (high, medium, and weak type) are extracted using a fuzzy set theoretic approach. Some invariant color features are computed from these points to evaluate the similarity between images. A set of relevant and non-redundant features is selected using the mutual information based minimum redundancy-maximum relevance framework. The relative importance of each feature is evaluated using a fuzzy entropy based measure, which is computed from the sets of retrieved images marked relevant and irrelevant by the users. The performance of the system is evaluated using different sets of examples from a general purpose image database. The robustness of the system is also shown when the images undergo different transformations.

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

[2]  Shyi-Chyi Cheng.  Content-based image retrieval using moment-preserving edge detection , 2003, Image Vis. Comput..

[3]  Joost van de Weijer,et al.  Boosting color saliency in image feature detection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Arnold W. M. Smeulders,et al.  PicToSeek: combining color and shape invariant features for image retrieval , 2000, IEEE Trans. Image Process..

[5]  Thomas S. Huang,et al.  Relevance feedback in content-based image retrieval: some recent advances , 2002, Inf. Sci..

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

[7]  Alberto Del Bimbo,et al.  Visual information retrieval , 1999 .

[8]  Dengsheng Zhang,et al.  An Efficient and Robust Technique for Region Based Shape Representation and Retrieval , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[9]  King Ngi Ngan,et al.  A memory learning framework for effective image retrieval , 2005, IEEE Transactions on Image Processing.

[10]  José M. Soto-Hidalgo,et al.  Retrieving images in fuzzy object-relational databases using dominant color descriptors , 2007, Fuzzy Sets Syst..

[11]  Jitendra Malik,et al.  Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.

[12]  Pradipta Maji,et al.  $f$-Information Measures for Efficient Selection of Discriminative Genes From Microarray Data , 2009, IEEE Transactions on Biomedical Engineering.

[13]  Xuequn Li,et al.  Content-Based Image Retrieval by Relevance Feedback , 2000, VISUAL.

[14]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[15]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[16]  Yixin Chen,et al.  A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Malay Kumar Kundu,et al.  Content Based Image Retrieval with Multiresolution Salient Points , 2004, ICVGIP.

[18]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[19]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[20]  Sankar K. Pal.  Fuzzy set theoretic measures for automatic feature evaluation: II , 1992, Inf. Sci..

[21]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Mausumi Acharyya,et al.  Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  Chin-Chen Chang,et al.  A new edge detection approach based on image context analysis , 2006, Image Vis. Comput..

[24]  Malay Kumar Kundu,et al.  Edge based features for content based image retrieval , 2003, Pattern Recognit..

[25]  C. Tomasi.  The Earth Mover's Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval , 1997 .

[26]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[27]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[28]  Carlo Tomasi,et al.  The Earth Mover’s Distance , 2001 .

[29]  Valérie Gouet,et al.  About optimal use of color points of interest for content-based image retrieval , 2002 .

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

[31]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[32]  Arnold W. M. Smeulders,et al.  Content-based image retrieval by viewpoint-invariant color indexing , 1999, Image Vis. Comput..

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

[34]  Sankar K. Pal,et al.  Fuzzy Mathematical Approach to Pattern Recognition , 1986 .

[35]  Nicu Sebe,et al.  Wavelet-Based Salient Points: Applications to Image Retrieval Using Color and Texture Features , 2000, VISUAL.

[36]  Basabi Chakraborty,et al.  Intraclass and interclass ambiguities (fuzziness) in feature evaluation , 1984, Pattern Recognit. Lett..

[37]  Arnold W. M. Smeulders,et al.  Color Based Object Recognition , 1997, ICIAP.

[38]  Azriel Rosenfeld,et al.  Fuzzy Digital Topology , 1979, Inf. Control..

[39]  Mausumi Acharyya,et al.  Extraction of Features Using M-Band Wavelet Packet Frame and Their Neuro-Fuzzy Evaluation for Multitexture Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[41]  Sang Uk Lee,et al.  Integrated Position Estimation Using Aerial Image Sequences , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Yixin Chen,et al.  CLUE: cluster-based retrieval of images by unsupervised learning , 2005, IEEE Transactions on Image Processing.

[43]  Thierry Pun,et al.  Content-based query of image databases: inspirations from text retrieval , 2000, Pattern Recognit. Lett..

[44]  Hui Zhang,et al.  Local image representations using pruned salient points with applications to CBIR , 2006, MM '06.

[45]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[46]  Shyi-Chyi Cheng,et al.  Visual object retrieval via block-based visual-pattern matching , 2007, Pattern Recognit..