Web-Scale Image Retrieval Using Compact Tensor Aggregation of Visual Descriptors

The main issues for Web-scale image retrieval are achieving good accuracy while retaining low computational time and memory footprint. This article proposes a compact image signature by aggregating tensors of visual descriptors. Efficient aggregation is achieved by preprocessing the descriptors. Compactness is achieved by projection and quantization of the signatures. The authors compare the proposed method to other efficient signatures on a 1 million images dataset and show the soundness of the approach.

[1]  Laurent Amsaleg,et al.  NV-Tree: nearest neighbors at the billion scale , 2011, ICMR '11.

[2]  Cor J. Veenman,et al.  Kernel Codebooks for Scene Categorization , 2008, ECCV.

[3]  Matthieu Cord,et al.  Efficient Bag-of-Feature kernel representation for image similarity search , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[5]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[8]  Sylvie Philipp-Foliguet,et al.  Kernels on bags for multi-object database retrieval , 2007, CIVR '07.

[9]  David Picard,et al.  Improving image similarity with vectors of locally aggregated tensors , 2011, 2011 18th IEEE International Conference on Image Processing.

[10]  Siwei Lyu,et al.  Mercer kernels for object recognition with local features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

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

[14]  Matthieu Cord,et al.  BOSSA: Extended bow formalism for image classification , 2011, 2011 18th IEEE International Conference on Image Processing.

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

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

[17]  Romain Negrel Représentations optimales pour la recherche dans les bases d'images patrimoniales , 2014 .

[18]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.