Incremental Hashing for Semantic Image Retrieval in Nonstationary Environments

A very large volume of images is uploaded to the Internet daily. However, current hashing methods for image retrieval are designed for static databases only. They fail to consider the fact that the distribution of images can change when new images are added to the database over time. The changes in the distribution of images include both discovery of a new class and a distribution of images within a class owing to concept drift. Retraining of hash tables using all images in the database requires a large computation effort. This is also biased to old data owing to the huge volume of old images which leads to a poor retrieval performance over time. In this paper, we propose the incremental hashing (ICH) method to deal with the two aforementioned types of changes in the data distribution. The ICH uses a multihashing to retain knowledge coming from images arriving over time and a weight-based ranking to make the retrieval results adaptive to the new data environment. Experimental results show that the proposed method is effective in dealing with changes in the database.

[1]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[2]  Kristen Grauman,et al.  Kernelized locality-sensitive hashing for scalable image search , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Wei-Shi Zheng,et al.  Online Hashing , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[4]  David J. Fleet,et al.  Cartesian K-Means , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Dewen Hu,et al.  "Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview. , 2013, IEEE transactions on cybernetics.

[6]  Wei-Shi Zheng,et al.  Smart Hashing Update for Fast Response , 2013, IJCAI.

[7]  Sanjiv Kumar,et al.  Angular Quantization-based Binary Codes for Fast Similarity Search , 2012, NIPS.

[8]  Shih-Fu Chang,et al.  Query-Adaptive Image Search With Hash Codes , 2013, IEEE Transactions on Multimedia.

[9]  Xuelong Li,et al.  Compressed Hashing , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Zi Huang,et al.  Robust Hashing With Local Models for Approximate Similarity Search , 2014, IEEE Transactions on Cybernetics.

[11]  Patrick P. K. Chan,et al.  Asymmetric Cyclical Hashing for Large Scale Image Retrieval , 2015, IEEE Transactions on Multimedia.

[12]  Xiangwei Kong,et al.  Large-scale image retrieval based on boosting iterative quantization hashing with query-adaptive reranking , 2013, Neurocomputing.

[13]  Xiao Zhang,et al.  QsRank: Query-sensitive hash code ranking for efficient ∊-neighbor search , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  David J. Fleet,et al.  Fast Exact Search in Hamming Space With Multi-Index Hashing , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Shih-Fu Chang,et al.  Semi-supervised hashing for scalable image retrieval , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Hanqing Lu,et al.  Online sketching hashing , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Deng Cai,et al.  Density Sensitive Hashing , 2012, IEEE Transactions on Cybernetics.

[18]  Xuelong Li,et al.  Compact Structure Hashing via Sparse and Similarity Preserving Embedding , 2016, IEEE Transactions on Cybernetics.

[19]  Shih-Fu Chang,et al.  Semi-Supervised Hashing for Large-Scale Search , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[21]  Xuelong Li,et al.  Large-Scale Unsupervised Hashing with Shared Structure Learning , 2015, IEEE Transactions on Cybernetics.

[22]  Yao Hu,et al.  Fast and Accurate Hashing Via Iterative Nearest Neighbors Expansion , 2014, IEEE Transactions on Cybernetics.

[23]  Yi Zhen,et al.  Active hashing and its application to image and text retrieval , 2012, Data Mining and Knowledge Discovery.

[24]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[25]  Pascal Fua,et al.  LDAHash: Improved Matching with Smaller Descriptors , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Svetlana Lazebnik,et al.  Locality-sensitive binary codes from shift-invariant kernels , 2009, NIPS.

[27]  Prateek Jain,et al.  Fast Similarity Search for Learned Metrics , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Svetlana Lazebnik,et al.  Asymmetric Distances for Binary Embeddings , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  David Suter,et al.  Fast Supervised Hashing with Decision Trees for High-Dimensional Data , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Robi Polikar,et al.  Incremental Learning of Concept Drift in Nonstationary Environments , 2011, IEEE Transactions on Neural Networks.

[31]  Patrick P. K. Chan,et al.  Two-phase mapping hashing , 2015, Neurocomputing.

[32]  Antonio Torralba,et al.  Spectral Hashing , 2008, NIPS.

[33]  Xuelong Li,et al.  Spectral Embedded Hashing for Scalable Image Retrieval , 2014, IEEE Transactions on Cybernetics.

[34]  Chun Chen,et al.  Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning , 2013, IEEE Transactions on Knowledge and Data Engineering.

[35]  Ling Shao,et al.  Unsupervised Local Feature Hashing for Image Similarity Search , 2016, IEEE Transactions on Cybernetics.

[36]  Svetlana Lazebnik,et al.  Iterative quantization: A procrustean approach to learning binary codes , 2011, CVPR 2011.