MULTIPLE DICTIONARIES FOR BAG OFWORDS LARGE SCALE IMAGE SEARCH

We explore building better dictionaries of visual words in Bag of Words (BoW) systems for searching large scale image collections. We propose a novel way to use more visual words in the BoW algorithm that significantly increases the recognition performance: combining multiple dictionaries built from different subsets of the features. We report improvement in performance by 40-45% over the baseline method and discuss how it can be parallelized on multiple machines to achieve faster run time. Finally, we show that our method is flexible, and provides 25-38% improvement over the startof-the-art Hamming Embedding method.

[1]  Andrew Zisserman,et al.  Near Duplicate Image Detection: min-Hash and tf-idf Weighting , 2008, BMVC.

[2]  Cordelia Schmid,et al.  Packing bag-of-features , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[4]  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.

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

[6]  Jian Sun,et al.  Bundling features for large scale partial-duplicate web image search , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[8]  Pietro Perona,et al.  Indexing in large scale image collections: Scaling properties and benchmark , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

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

[10]  M. Aly,et al.  Online learning for parameter selection in large scale image search , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[11]  Pietro Perona,et al.  Towards automated large scale discovery of image families , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[12]  O. Chum,et al.  Geometric min-Hashing: Finding a (thick) needle in a haystack , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[15]  Michael Isard,et al.  General Theory , 1969 .

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

[17]  JUSTIN ZOBEL,et al.  Inverted files for text search engines , 2006, CSUR.