A relevance feedback method based on genetic programming for classification of remote sensing images

This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learning user preferences and combining image region descriptors that encode spectral and texture properties. Experiments demonstrate that the proposed method is effective for image classification tasks and outperforms the traditional MaxVer method.

[1]  David B. Fogel,et al.  Evolution-ary Computation 1: Basic Algorithms and Operators , 2000 .

[2]  Weiguo Fan,et al.  A generic ranking function discovery framework by genetic programming for information retrieval , 2004, Inf. Process. Manag..

[3]  Ricardo da Silva Torres,et al.  Recuperação de imagens com realimentação de relevancia baseada em programação genetica , 2007 .

[4]  Bo Zhang,et al.  Relevance feedback in region-based image retrieval , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Chiou-Shann Fuh,et al.  Local Ensemble Kernel Learning for Object Category Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[7]  Edward A. Fox,et al.  Combining structural and citation-based evidence for text classification , 2004, CIKM '04.

[8]  Jérome Fournier,et al.  Exploration and search-by-similarity in CBIR , 2003, 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2003).

[9]  João Paulo Papa,et al.  Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images , 2008, EURASIP J. Adv. Signal Process..

[10]  Woo-Cheol Kim,et al.  Image retrieval model based on weighted visual features determined by relevance feedback , 2008, Inf. Sci..

[11]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[12]  Montse Pardàs,et al.  Deleted DOI: Audiovisual Head Orientation Estimation with Particle Filtering in Multisensor Scenarios , 2008 .

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

[14]  Thomas S. Huang,et al.  Optimizing learning in image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  Alexandre X. Falcão,et al.  The Ordered Queue and the Optimality of the Watershed Approaches , 2000, ISMM.

[16]  Yasufumi Takama,et al.  Genetic algorithms for a family of image similarity models incorporated in the relevance feedback mechanism , 2003, Appl. Soft Comput..

[17]  Edward A. Fox,et al.  A new framework to combine descriptors for content-based image retrieval , 2005, CIKM '05.

[18]  R. A. Schonengertt Techniques for Image Processing and Classification in Remote Sensing , 1983 .

[19]  Bir Bhanu,et al.  Object detection in multi-modal images using genetic programming , 2004, Appl. Soft Comput..

[20]  Ricardo da Silva Torres,et al.  Content-Based Image Retrieval: Theory and Applications , 2006, RITA.

[21]  Weiguo Fan,et al.  Image Retrieval with Relevance Feedback based on Genetic Programming , 2008, SBBD.

[22]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[23]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Mario A. Nascimento,et al.  A compact and efficient image retrieval approach based on border/interior pixel classification , 2002, CIKM '02.

[25]  B. C. Brookes,et al.  Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.

[26]  Neucimar J. Leite,et al.  Wavelet-based Feature Extraction for Fingerprint Image Retrieval , 2007 .

[27]  Qi Tian,et al.  Incorporate support vector machines to content-based image retrieval with relevance feedback , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[28]  Michael Unser,et al.  A family of polynomial spline wavelet transforms , 1993, Signal Process..

[29]  Yasufumi Takama,et al.  Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback , 2005, Signal Process..

[30]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[31]  Hui Lin,et al.  Design and Implementation of a High Spatial Resolution Remote Sensing Image Intelligent Interpretation System , 2007, Data Sci. J..

[32]  Harris Wu,et al.  The effects of fitness functions on genetic programming-based ranking discovery for Web search: Research Articles , 2004 .

[33]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[34]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[35]  Robert A. Schowengerdt,et al.  Techniques for image processing and classification in remote sensing , 1983 .

[36]  Nachol Chaiyaratana,et al.  Thalassaemia classification by neural networks and genetic programming , 2007, Inf. Sci..

[37]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[38]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[39]  Chris North,et al.  Design and Evaluation of Techniques to Utilize Implicit Rating Data in Complex Information Systems. , 2007 .

[40]  Yasufumi Takama,et al.  Relevance feedback-based image retrieval interface incorporating region and feature saliency patterns as visualizable image similarity criteria , 2003, IEEE Trans. Ind. Electron..

[41]  Antonio J. Rivera,et al.  GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems , 2010, Inf. Sci..

[42]  Do-Hyung Kim,et al.  Comparison of Three Land Cover Classification Algorithms -ISODATA, SMA, and SOM - for the Monitoring of North Korea with MODIS Multi-temporal Data , 2007 .

[43]  Isa Yildirim,et al.  Improvement of classification accuracy in remote sensing using morphological filter , 2005 .

[44]  Neucimar J. Leite,et al.  Wavelet-based fingerprint image retrieval , 2009 .

[45]  Wen Gao,et al.  Adaptive relevance feedback based on Bayesian inference for image retrieval , 2005, Signal Process..

[46]  Cheng Wang,et al.  A novel extended local-binary-pattern operator for texture analysis , 2008, Inf. Sci..

[47]  Edward A. Fox,et al.  A genetic programming framework for content-based image retrieval , 2009, Pattern Recognit..

[48]  Jefersson Alex dos Santos,et al.  A Genetic Programming Approach for Relevance Feedback in Region-Based Image Retrieval Systems , 2008, 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing.