Hybrid-Indexing Multi-type Features for Large-Scale Image Search

Indexing local features with a vocabulary tree and indexing holistic features by compact hashing codes are two successful but separated lines of research. Both of the two indexing models are suited for specific features and are limited to certain scenarios like partial-duplicate search and similar image search, respectively. To conquer such limitations, we propose a novel hybrid-indexing strategy, which incorporates multiple similarity metrics into one inverted index file during off-line indexing. Hybrid-Indexing only requires the Bag-of-visual Words (BoWs) model as input for online query, but could obtain more satisfying retrieval results because the index file conveys hybrid similarities among images. Moreover, hybrid-indexing does not degrade the efficiency of classic BoWs based image search. Experiments on several public datasets manifest the effectiveness and efficiency of our proposed method.

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

[2]  Andrew W. Fitzgibbon,et al.  Efficient Object Category Recognition Using Classemes , 2010, ECCV.

[3]  Cordelia Schmid,et al.  Accurate Image Search Using the Contextual Dissimilarity Measure , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Qi Tian,et al.  Scalar quantization for large scale image search , 2012, ACM Multimedia.

[5]  Mark J. Huiskes,et al.  The MIR flickr retrieval evaluation , 2008, MIR '08.

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

[7]  Fei-Fei Li,et al.  Hierarchical semantic indexing for large scale image retrieval , 2011, CVPR 2011.

[8]  Shiliang Zhang,et al.  Superimage: Packing Semantic-Relevant Images for Indexing and Retrieval , 2014, ICMR.

[9]  Ming Yang,et al.  Query Specific Fusion for Image Retrieval , 2012, ECCV.

[10]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[11]  Yang Yu,et al.  Automatic image annotation using group sparsity , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Shiliang Zhang,et al.  USB: Ultrashort Binary Descriptor for Fast Visual Matching and Retrieval , 2014, IEEE Transactions on Image Processing.

[13]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[14]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 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]  Shiliang Zhang,et al.  Semantic-Aware Co-Indexing for Image Retrieval. , 2015, IEEE transactions on pattern analysis and machine intelligence.

[17]  Nicu Sebe,et al.  Image retrieval using wavelet-based salient points , 2001, J. Electronic Imaging.

[18]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[19]  Cordelia Schmid,et al.  Combining attributes and Fisher vectors for efficient image retrieval , 2011, CVPR 2011.

[20]  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.

[21]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[24]  Gang Hua,et al.  Descriptive visual words and visual phrases for image applications , 2009, ACM Multimedia.

[25]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[26]  Shiliang Zhang,et al.  Embedding Multi-Order Spatial Clues for Scalable Visual Matching and Retrieval , 2014, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

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

[28]  Dong Liu,et al.  Robust late fusion with rank minimization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Sebastian Nowozin,et al.  On feature combination for multiclass object classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[31]  Ronald Fagin,et al.  Efficient similarity search and classification via rank aggregation , 2003, SIGMOD '03.