Hierarchical Image Retrieval by Multi-Feature Fusion

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

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

[2]  Victor S. Lempitsky,et al.  Neural Codes for Image Retrieval , 2014, ECCV.

[3]  P.S. Hiremath,et al.  Content Based Image Retrieval Using Color, Texture and Shape Features , 2007, 15th International Conference on Advanced Computing and Communications (ADCOM 2007).

[4]  Qi Tian,et al.  Query-adaptive late fusion for image search and person re-identification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[7]  Shiliang Zhang,et al.  Semantic-Aware Co-Indexing for Image Retrieval. , 2015, IEEE transactions on pattern analysis and machine intelligence.

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

[9]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

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

[11]  Victor S. Lempitsky,et al.  Aggregating Local Deep Features for Image Retrieval , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[12]  Florent Perronnin,et al.  Fisher vectors meet Neural Networks: A hybrid classification architecture , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Svetlana Lazebnik,et al.  Multi-scale Orderless Pooling of Deep Convolutional Activation Features , 2014, ECCV.

[14]  Jen-Hao Hsiao,et al.  Deep learning of binary hash codes for fast image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[17]  C. Schmid,et al.  Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search - extended version , 2008 .

[18]  Xi Zhang,et al.  Feature integration analysis of bag-of-features model for image retrieval , 2013, Neurocomputing.

[19]  Ernest Valveny,et al.  Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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