Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images and achieves high recognition rates despite its conceptual simplicity and computational efficiency.

[1]  Ian D. Reid,et al.  Real-Time SLAM Relocalisation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Stepán Obdrzálek,et al.  Sub-linear Indexing for Large Scale Object Recognition , 2005, BMVC.

[3]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[4]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Raphaël Marée,et al.  Random subwindows for robust image classification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Hermann Ney,et al.  The CLEF 2005 Automatic Medical Image Annotation Task , 2006, International Journal of Computer Vision.

[8]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Vincent Lepetit,et al.  Fast Keypoint Recognition in Ten Lines of Code , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Frédéric Jurie,et al.  Randomized Clustering Forests for Image Classification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Frédéric Jurie,et al.  Learning Visual Similarity Measures for Comparing Never Seen Objects , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  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).

[13]  Hermann Ney,et al.  Classification error rate for quantitative evaluation of content-based image retrieval systems , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Michael Kohnen,et al.  The IRMA code for unique classification of medical images , 2003, SPIE Medical Imaging.

[15]  Luc Van Gool,et al.  Fast indexing for image retrieval based on local appearance with re-ranking , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[16]  Pierre Geurts,et al.  Kernelizing the output of tree-based methods , 2006, ICML '06.

[17]  情報処理学会.  IPSJ transactions on computer vision and applications , 2009 .

[18]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[19]  Christian Böhm,et al.  Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases , 2001, CSUR.

[20]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1976, TOMS.

[21]  Raphaël Marée,et al.  An empirical comparison of machine learning algorithms for generic image classification , 2003 .

[22]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[23]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2006 .

[24]  Stepán Obdrzálek,et al.  Image Retrieval Using Local Compact DCT-Based Representation , 2003, DAGM-Symposium.

[25]  Hermann Ney,et al.  Discriminative training for object recognition using image patches , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[26]  Raphaël Marée,et al.  Biomedical Image Classification with Random Subwindows and Decision Trees , 2005, CVBIA.

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

[28]  Trevor Darrell,et al.  The Pyramid Match Kernel: Efficient Learning with Sets of Features , 2007, J. Mach. Learn. Res..

[29]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.