Region similarity arrangement for image retrieval

We propose a promising method of geometric verification to improve the precision of Bag-of-Words (BoW) model in image retrieval. Most previous methods focus on the positions of interest points or the absolute differences of regions' scales and angles. In contrast, our method, named Region Similarity Arrangement (RSA), exploits the spatial arrangement of interest regions. For each image, RSA constructs a Region Property Space, regarding each region's (scale, angle) pair as a point in a polar coordinate system, and encodes the arrangement of these points into the BoW vector. From experimental results on Holidays, Oxford5K and Paris, RSA could get comparable results with sate-of-the-art methods. In addition, RSA increases no extra memory and negligible computational consumption compared with the baseline BoW approach.

[1]  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).

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

[3]  Ricardo da Silva Torres,et al.  Visual word spatial arrangement for image retrieval and classification , 2014, Pattern Recognit..

[4]  Ming Yang,et al.  Contextual weighting for vocabulary tree based image retrieval , 2011, 2011 International Conference on Computer Vision.

[5]  Changhu Wang,et al.  Spatial-bag-of-features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Steven C. H. Hoi,et al.  Fast Object Retrieval Using Direct Spatial Matching , 2015, IEEE Transactions on Multimedia.

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

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

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

[12]  Ian H. Witten,et al.  Managing Gigabytes: Compressing and Indexing Documents and Images , 1999 .

[13]  Qi Tian,et al.  Spatial coding for large scale partial-duplicate web image search , 2010, ACM Multimedia.

[14]  Andrew Zisserman,et al.  Triangulation Embedding and Democratic Aggregation for Image Search , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  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.

[16]  Michael Isard,et al.  Bundling features for large scale partial-duplicate web image search , 2009, CVPR.

[17]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

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

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

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