Efficient continuous top-k geo-image search on road network

With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-k geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and road network distance proximity on road network. In order to address this challenge effectively and efficiently, firstly we propose the definition of geo-visual objects and continuous top-k geo-visual objects query on road network, then develop a score function for search. To improve the query efficiency in a large-scale road network, we propose the search algorithm named geo-visual search on road network based on a novel hybrid indexing framework called VIG-Tree, which combines G-Tree and visual inverted index technique. In addition, an important notion named safe interval and results updating rule are proposed, and based on them we develop an efficient algorithm named moving monitor algorithm to solve continuous query. Experimental evaluation on real multimedia dataset and road network dataset illustrates that our solution outperforms state-of-the-art method.

[1]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS '01.

[2]  Lin Wu,et al.  Deep adaptive feature embedding with local sample distributions for person re-identification , 2017, Pattern Recognit..

[3]  Lin Wu,et al.  Shifting Hypergraphs by Probabilistic Voting , 2014, PAKDD.

[4]  Jianliang Xu,et al.  Reverse keyword search for spatio-textual top-k queries in location-based services , 2015, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[5]  Muhammad Aamir Cheema,et al.  Diversified Spatial Keyword Search On Road Networks , 2014, EDBT.

[6]  Jianjun Li,et al.  Efficient reverse spatial and textual k nearest neighbor queries on road networks , 2016, Knowl. Based Syst..

[7]  Lin Wu,et al.  LBMCH: Learning Bridging Mapping for Cross-modal Hashing , 2015, SIGIR.

[8]  Christian S. Jensen,et al.  A framework for efficient spatial web object retrieval , 2012, The VLDB Journal.

[9]  Christian S. Jensen,et al.  Joint Top-K Spatial Keyword Query Processing , 2012, IEEE Transactions on Knowledge and Data Engineering.

[10]  Lin Wu,et al.  Exploiting Correlation Consensus: Towards Subspace Clustering for Multi-modal Data , 2014, ACM Multimedia.

[11]  Lin Wu,et al.  Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval , 2017, IEEE Transactions on Image Processing.

[12]  Lin Wu,et al.  Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Naphtali Rishe,et al.  Keyword Search on Spatial Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[14]  Yang Wang,et al.  Towards metric fusion on multi-view data: a cross-view based graph random walk approach , 2013, CIKM.

[15]  Hanan Samet,et al.  Distance browsing in spatial databases , 1999, TODS.

[16]  Yang Wang,et al.  Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification , 2017, Comput. Vis. Image Underst..

[17]  Linda G. Shapiro,et al.  A SIFT descriptor with global context , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Htoo Htet Aung,et al.  Efficient continuous top-k spatial keyword queries on road networks , 2014, GeoInformatica.

[19]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  Feifei Li,et al.  Approximate string search in spatial databases , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[21]  Lin Wu,et al.  Effective Multi-Query Expansions: Robust Landmark Retrieval , 2015, ACM Multimedia.

[22]  Lin Wu,et al.  Efficient image and tag co-ranking: a bregman divergence optimization method , 2013, ACM Multimedia.

[23]  Kian-Lee Tan,et al.  Efficient safe-region construction for moving top-K spatial keyword queries , 2012, CIKM.

[24]  Xiaojun Qi,et al.  Complementary relevance feedback-based content-based image retrieval , 2014, Multimedia Tools and Applications.

[25]  Xue Li,et al.  Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition , 2019, IEEE Transactions on Cybernetics.

[26]  Yu Liu,et al.  Multi-focus image fusion with dense SIFT , 2015, Inf. Fusion.

[27]  Lin Wu,et al.  Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions , 2016, Image Vis. Comput..

[28]  Lin Wu,et al.  Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus , 2015, IEEE Transactions on Image Processing.

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

[30]  Wang Yi,et al.  Processing Moving kNN Queries Using Influential Neighbor Sets , 2014, Proc. VLDB Endow..

[31]  Xuemin Lin,et al.  Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search , 2016, IEEE Transactions on Knowledge and Data Engineering.

[32]  Anthony K. H. Tung,et al.  Locating mapped resources in Web 2.0 , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[33]  Naphtali Rishe,et al.  Efficient and Scalable Method for Processing Top-k Spatial Boolean Queries , 2010, SSDBM.

[34]  David J. Fleet,et al.  Hamming Distance Metric Learning , 2012, NIPS.

[35]  Chen Li,et al.  Supporting location-based approximate-keyword queries , 2010, GIS '10.

[36]  Ping Wang,et al.  Content-based image retrieval based on CNN and SVM , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[37]  Ken C. K. Lee,et al.  IR-Tree: An Efficient Index for Geographic Document Search , 2011, IEEE Trans. Knowl. Data Eng..

[38]  Kian-Lee Tan,et al.  Processing spatial keyword query as a top-k aggregation query , 2014, SIGIR.

[39]  Lin Wu,et al.  Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering , 2016, IJCAI.

[40]  Hai Jin,et al.  Content-Based Visual Landmark Search via Multimodal Hypergraph Learning , 2015, IEEE Transactions on Cybernetics.

[41]  Xing Xie,et al.  Hybrid index structures for location-based web search , 2005, CIKM '05.

[42]  Bart Thomee,et al.  Interactive search in image retrieval: a survey , 2012, International Journal of Multimedia Information Retrieval.

[43]  Andrew Zisserman,et al.  Near Duplicate Image Detection: min-Hash and tf-idf Weighting , 2008, BMVC.

[44]  Christian S. Jensen,et al.  Efficient continuously moving top-k spatial keyword query processing , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[45]  Christian S. Jensen,et al.  Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects , 2009, Proc. VLDB Endow..

[46]  Torsten Suel,et al.  Text vs. space: efficient geo-search query processing , 2011, CIKM '11.

[47]  Gang Chen,et al.  Efficient Reverse Top-k Boolean Spatial Keyword Queries on Road Networks , 2015, IEEE Transactions on Knowledge and Data Engineering.

[48]  João B. Rocha-Junior,et al.  Top-k spatial keyword queries on road networks , 2012, EDBT '12.

[49]  Yuan Zhang,et al.  SIFT Matching with CNN Evidences for Particular Object Retrieval , 2017, Neurocomputing.

[50]  João B. Rocha-Junior,et al.  Efficient Processing of Top-k Spatial Keyword Queries , 2011, SSTD.

[51]  Anthony K. H. Tung,et al.  Scalable top-k spatial keyword search , 2013, EDBT '13.

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

[53]  Lin Wu,et al.  Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering , 2017, Neural Networks.

[54]  Anthony K. H. Tung,et al.  Keyword Search in Spatial Databases: Towards Searching by Document , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[55]  Pengpeng Zhao,et al.  Effective Spatial Keyword Query Processing on Road Networks , 2015, ADC.

[56]  Lin Wu,et al.  Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[57]  Chen Li,et al.  Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems , 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).

[58]  Lin Wu,et al.  What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification , 2017, Pattern Recognit..

[59]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[60]  Aun Irtaza,et al.  Content based image retrieval in a web 3.0 environment , 2013, Multimedia Tools and Applications.