Efficient Image Detail Mining

Two novel problems straddling the boundary between image retrieval and data mining are formulated: for every pixel in the query image, (i) find the database image with the maximum resolution depicting the pixel and (ii) find the frequency with which it is photographed in detail.

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

[2]  Jiri Matas,et al.  Total recall II: Query expansion revisited , 2011, CVPR 2011.

[3]  Andrew Zisserman,et al.  Three things everyone should know to improve object retrieval , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Julien Pilet,et al.  Size Matters: Exhaustive Geometric Verification for Image Retrieval Accepted for ECCV 2012 , 2012, ECCV.

[5]  Jiri Matas,et al.  Efficient representation of local geometry for large scale object retrieval , 2009, CVPR.

[6]  Jiri Matas,et al.  Image Retrieval for Online Browsing in Large Image Collections , 2013, SISAP.

[7]  Cordelia Schmid,et al.  A contextual dissimilarity measure for accurate and efficient image search , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[9]  Michael Isard,et al.  Lost in quantization: Improving particular object retrieval in large scale image databases , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  Jiri Matas,et al.  Large-Scale Discovery of Spatially Related Images , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Tomás Pajdla,et al.  Avoiding Confusing Features in Place Recognition , 2010, ECCV.

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

[14]  C. Schmid,et al.  On the burstiness of visual elements , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Jiri Matas,et al.  Learning Vocabularies over a Fine Quantization , 2013, International Journal of Computer Vision.

[16]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

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

[19]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[20]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[21]  Gang Hua,et al.  Picking the best DAISY , 2009, CVPR.

[22]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[23]  Bastian Leibe,et al.  Discovering Details and Scene Structure with Hierarchical Iconoid Shift , 2013, 2013 IEEE International Conference on Computer Vision.

[24]  Michael Isard,et al.  Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[25]  Jiri Matas,et al.  Locally Optimized RANSAC , 2003, DAGM-Symposium.

[26]  Hervé Jégou,et al.  Negative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening , 2012, ECCV.

[27]  Florent Perronnin,et al.  Large-scale image retrieval with compressed Fisher vectors , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Jiri Matas,et al.  Unsupervised discovery of co-occurrence in sparse high dimensional data , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[29]  Cordelia Schmid,et al.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.