Web Image Organization and Object Discovery by Actively Creating Visual Clusters through Crowdsourcing

In this paper, we propose to organize web images by actively creating visual clusters via crowd sourcing. We develop a two-phase framework to efficiently and effectively combine computers and a large number of human workers to build high quality visual clusters. The first phase partitions an image collection into multiple clusters, the second phase refines each generated cluster independently. In both phases, informative images are selected by computers and manually labeled by the crowds to learn improved models. Our method can be naturally extended to discover object categories in a collection of image segments. Experimental results on several data sets demonstrate the promise of our developed approach on both web image organization and object discovery tasks.

[1]  Christos Faloutsos,et al.  Unsupervised modeling of object categories using link analysis techniques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  eon BottouAT.  Stochastic Gradient Learning in Neural Networks , 2022 .

[3]  Yong Jae Lee,et al.  Foreground Focus: Unsupervised Learning from Partially Matching Images , 2009, International Journal of Computer Vision.

[4]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[5]  Rong Jin,et al.  Bayesian Active Distance Metric Learning , 2007, UAI.

[6]  Kristen Grauman,et al.  Large-scale live active learning: Training object detectors with crawled data and crowds , 2011, CVPR.

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

[8]  Kristen Grauman,et al.  What's it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations , 2009, CVPR.

[9]  Vladimir Pavlovic,et al.  A New Baseline for Image Annotation , 2008, ECCV.

[10]  Xing Xie,et al.  Effective browsing of web image search results , 2004, MIR '04.

[11]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[12]  Kristen Grauman,et al.  Multi-Level Active Prediction of Useful Image Annotations for Recognition , 2008, NIPS.

[13]  Gert R. G. Lanckriet,et al.  From region similarity to category discovery , 2011, CVPR 2011.

[14]  Wei-Ying Ma,et al.  Iteratively clustering web images based on link and attribute reinforcements , 2005, ACM Multimedia.

[15]  Pietro Perona,et al.  Crowdclustering , 2011, NIPS.

[16]  Thorsten Joachims,et al.  Learning a Distance Metric from Relative Comparisons , 2003, NIPS.

[17]  Pietro Perona,et al.  Strong supervision from weak annotation: Interactive training of deformable part models , 2011, 2011 International Conference on Computer Vision.

[18]  Trevor Darrell,et al.  Unsupervised Learning of Categories from Sets of Partially Matching Image Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Turk Paul Wais,et al.  Towards Building a High-Quality Workforce with Mechanical , 2010 .

[20]  William A. Gale,et al.  A sequential algorithm for training text classifiers , 1994, SIGIR '94.

[21]  Tsuhan Chen,et al.  Unsupervised Image Categorization and Object Localization using Topic Models and Correspondences between Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  Wei-Ying Ma,et al.  Hierarchical clustering of WWW image search results using visual, textual and link information , 2004, MULTIMEDIA '04.

[23]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

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

[25]  Ximena Olivares,et al.  Visual diversification of image search results , 2009, WWW '09.

[26]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Wei-Ying Ma,et al.  Grouping web image search result , 2004, MULTIMEDIA '04.

[28]  Tao Qin,et al.  Web image clustering by consistent utilization of visual features and surrounding texts , 2005, MULTIMEDIA '05.

[29]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[30]  Philippe A. Palanque,et al.  Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 2014, International Conference on Human Factors in Computing Systems.

[31]  Jaime G. Carbonell,et al.  A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy , 2010, SDM.

[32]  Antonio Criminisi,et al.  Harvesting Image Databases from the Web , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[33]  Wei-Ying Ma,et al.  IGroup: web image search results clustering , 2006, MM '06.

[34]  Alexei A. Efros,et al.  Using Multiple Segmentations to Discover Objects and their Extent in Image Collections , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[35]  Yuandong Tian,et al.  A Face Annotation Framework with Partial Clustering and Interactive Labeling , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Yong Jae Lee,et al.  Object-graphs for context-aware category discovery , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.