Indexing Vectors of Locally Aggregated Descriptors Using Inverted Files

Vector of locally aggregated descriptors (VLAD) is a promising approach for addressing the problem of image search on a very large scale. This representation is proposed to overcome the quantization error problem faced in Bag-of-Words (BoW) representation. In this paper, we propose to enable inverted files of standard text search engines to exploit VLAD representation to deal with large-scale image search scenarios. We show that the use of inverted files with VLAD significantly outperforms BoW in terms of efficiency and effectiveness on the same hardware and software infrastructure.

[1]  Claudio Gennaro,et al.  An Approach to Content-Based Image Retrieval Based on the Lucene Search Engine Library , 2010, ECDL.

[2]  Cordelia Schmid,et al.  Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[4]  Gonzalo Navarro,et al.  Effective Proximity Retrieval by Ordering Permutations , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

[9]  Pavel Zezula,et al.  Similarity Search: The Metric Space Approach (Advances in Database Systems) , 2005 .

[10]  Andrea Esuli MiPai: Using the PP-Index to Build an Efficient and Scalable Similarity Search System , 2009, 2009 Second International Workshop on Similarity Search and Applications.

[11]  Pasquale Savino,et al.  Approximate similarity search in metric spaces using inverted files , 2008, Infoscale.

[12]  Claudio Gennaro,et al.  Combining local and global visual feature similarity using a text search engine , 2011, 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI).

[13]  Pavel Zezula,et al.  Similarity Search - The Metric Space Approach , 2005, Advances in Database Systems.

[14]  Xiao Zhang,et al.  Efficient indexing for large scale visual search , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

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