Query Adaptive Similarity for Large Scale Object Retrieval

Many recent object retrieval systems rely on local features for describing an image. The similarity between a pair of images is measured by aggregating the similarity between their corresponding local features. In this paper we present a probabilistic framework for modeling the feature to feature similarity measure. We then derive a query adaptive distance which is appropriate for global similarity evaluation. Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework. Experimental results show that our method improves the retrieval accuracy significantly and consistently. Moreover, our result compares favorably to the state-of-the-art.

[1]  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.

[2]  Bohyung Han,et al.  A fast nearest neighbor search algorithm by nonlinear embedding , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[4]  Cordelia Schmid,et al.  Product Quantization for Nearest Neighbor Search , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ying Wu,et al.  Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[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]  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.

[8]  Jiri Matas,et al.  Learning a Fine Vocabulary , 2010, ECCV.

[9]  Michael Isard,et al.  Descriptor Learning for Efficient Retrieval , 2010, ECCV.

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

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

[12]  Cordelia Schmid,et al.  Improving Bag-of-Features for Large Scale Image Search , 2010, International Journal of Computer Vision.

[13]  Hervé Jégou,et al.  Asymmetric Hamming Embedding , 2011, MM 2011.

[14]  Victor Lempitsky,et al.  The inverted multi-index , 2012, CVPR.

[15]  Ales Leonardis,et al.  High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[17]  C. Schmid,et al.  Exploiting descriptor distances for precise image search , 2011 .

[18]  Patrick Gros,et al.  Asymmetric hamming embedding: taking the best of our bits for large scale image search , 2011, ACM Multimedia.

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

[20]  Jean-Michel Morel,et al.  ASIFT: An Algorithm for Fully Affine Invariant Comparison , 2011, Image Process. Line.

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

[22]  Luc Van Gool,et al.  Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors , 2011, CVPR 2011.

[23]  JegouHerve,et al.  Improving Bag-of-Features for Large Scale Image Search , 2010 .

[24]  Matthijs Douze,et al.  Searching in one billion vectors: Re-rank with source coding , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Tsuhan Chen,et al.  Image retrieval with geometry-preserving visual phrases , 2011, CVPR 2011.

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

[27]  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.