Assisting Attraction Classification by Harvesting Web Data

Intelligent travel guide systems have grown increasingly popular in recent years. They also benefit a lot from the development of social media, resulting in a large amount of attractions uploaded by users. To tackle this, attractions should be real time classified by user-generated photos automatically to gain better user experience. However, in practice, the given label of photos and text ratings may be incomplete or missing. Moreover, recently, domain adaptation has been applied to deal with few labeled data. Thus, in this paper, we propose a novel framework for automatically attraction classification in leveraging web-harvesting data from search engine and the photos of attractions uploaded by users. Specifically, we assume that top-k web-harvesting images from search engines have correct labels. The classification problem is formulated as a regularized domain adaptation approach. Experiments conducted on the collected real-world data set demonstrated that the promising performance is gained over state-of-the art classification methods.

[1]  Jia Chen,et al.  DLMSearch: diversified landmark search by photo , 2012, ACM Multimedia.

[2]  Vijayan Sugumaran,et al.  Building the search pattern of web users using conceptual semantic space model , 2016, Int. J. Web Grid Serv..

[3]  Gang Hua,et al.  Context aware topic model for scene recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[5]  Jiang-Ming Yang,et al.  Generating location overviews with images and tags by mining user-generated travelogues , 2009, ACM Multimedia.

[6]  Stevan Rudinac,et al.  Finding representative and diverse community contributed images to create visual summaries of geographic areas , 2011, MM '11.

[7]  Chao Liu,et al.  A probabilistic approach to spatiotemporal theme pattern mining on weblogs , 2006, WWW '06.

[8]  Karsten M. Borgwardt,et al.  Covariate Shift by Kernel Mean Matching , 2009, NIPS 2009.

[9]  Mehryar Mohri,et al.  Sample Selection Bias Correction Theory , 2008, ALT.

[10]  Jianxin Wu,et al.  Person Re-Identification with Correspondence Structure Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Qi Tian,et al.  Image search reranking with multi-latent topical graph , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[13]  Steven M. Seitz,et al.  Scene Summarization for Online Image Collections , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Tao Mei,et al.  Finding perfect rendezvous on the go: accurate mobile visual localization and its applications to routing , 2012, ACM Multimedia.

[15]  Dong Xu,et al.  Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Jie Yu,et al.  The Mobile Media Based Emergency Management of Web Events Influence in Cyber-Physical Space , 2017, Wirel. Pers. Commun..

[17]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[18]  Fei-Fei Li,et al.  Learning latent temporal structure for complex event detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Pinar Duygulu Sahin,et al.  Re-ranking of web image search results using a graph algorithm , 2008, 2008 19th International Conference on Pattern Recognition.

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

[21]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[22]  Fei Gao,et al.  Biologically inspired image quality assessment , 2016, Signal Process..

[23]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[24]  Ling Shao,et al.  Transfer Learning for Visual Categorization: A Survey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[26]  Wei-Ying Ma,et al.  Diversifying landmark image search results by learning interested views from community photos , 2010, WWW '10.

[27]  Mor Naaman,et al.  Generating diverse and representative image search results for landmarks , 2008, WWW.

[28]  Xuelong Li,et al.  Learning to Rank for Blind Image Quality Assessment , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Jianfeng Gao,et al.  Domain Adaptation via Pseudo In-Domain Data Selection , 2011, EMNLP.

[30]  Jun Huan,et al.  Large margin transductive transfer learning , 2009, CIKM.

[31]  Massimiliano Pontil,et al.  Regularized multi--task learning , 2004, KDD.

[32]  Junchi Yan,et al.  Visual Saliency Detection via Sparsity Pursuit , 2010, IEEE Signal Processing Letters.

[33]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.