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Image search—from thousands to billions in 20 years

Published: 17 October 2013 Publication History

Abstract

This article presents a comprehensive review and analysis on image search in the past 20 years, emphasizing the challenges and opportunities brought by the astonishing increase of dataset scales from thousands to billions in the same time period, which was witnessed first-hand by the authors as active participants in this research area. Starting with a retrospective review of three stages of image search in the history, the article highlights major breakthroughs around the year 2000 in image search features, indexing methods, and commercial systems, which marked the transition from stage two to stage three. Subsequent sections describe the image search research from four important aspects: system framework, feature extraction and image representation, indexing, and big data's potential. Based on the review, the concluding section discusses open research challenges and suggests future research directions in effective visual representation, image knowledge base construction, implicit user feedback and crowdsourcing, mobile image search, and creative multimedia interfaces.

References

[1]
Ahonen, T., Hadid, A., and Pietikäinen, M. 2004. Face recognition with local binary patterns. In Proceedings of the 8th European Conference on Computer Vision. 469--481.
[2]
AOA. 2006. Good vision throughout life. http://www.aoa.org/x9419.xml.
[3]
Bay, H., Tuytelaars, T., and Van Gool, L. 2006. Surf: Speeded up robust features. In Proceedings of the 9th European Conference on Computer Vision. 404--417.
[4]
Blaser, A. 1979. Database techniques for pictorial applications. In Database Techniques for Pictorial Applications, Lecture Notes in Computer Science, vol. 81, Springer, Berlin.
[5]
Cao, Y., Wang, C., Li, Z., Zhang, L., and Zhang, L. 2010a. Spatial-bag-of-features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3352--3359.
[6]
Cao, Y., Wang, C., Zhang, L., and Zhang, L. 2011. Edgel index for large-scale sketch-based image search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 761--768.
[7]
Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., and Zhang, L. 2010b. Mindfinder: Interactive sketch-based image search on millions of images (demo). In Proceedings of the International Conference on Multimedia. ACM, 1605--1608.
[8]
Chang, N. and Fu, K. 1980a. Query-by-pictorial-example. IEEE Trans. Softw. Eng. 6, 519--524.
[9]
Chang, N. and Fu, K. 1980b. A relational database system for images. In Pictorial Information Systems, Lecture Notes in Computer Science, vol. 80, Springer, Berlin, 288--321.
[10]
Chang, S. and Kunil, T. 1981. Pictorial data-base systems. Computer 14, 11, 13--21.
[11]
Chang, S., Yan, C., Dimitroff, D., and Arndt, T. 1988. An intelligent image database system. IEEE Trans. Softw. Eng. 14, 5, 681--688.
[12]
Chum, O., Perdoch, M., and Matas, J. 2009. Geometric min-hashing: Finding a (thick) needle in a haystack. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 17--24.
[13]
Dalal, N. and Triggs, B. 2005. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vol. 1, IEEE, 886--893.
[14]
Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the 20th Annual Symposium on Computational Geometry. ACM, 253--262.
[15]
Datta, R., Joshi, D., Li, J., and Wang, J. 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40, 2, 5.
[16]
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 248--255.
[17]
Dong, W., Charikar, M., and Li, K. 2008. Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces. In Proceedings of the 31st Annual ACM SIGIR Conference. ACM, 123--130.
[18]
Faloutsos, C. and Taubin, G. 1993. The QBIC project: Querying images by content using color, texture, and shape. In Proceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases. Vol. 1908, 173--187.
[19]
Fan, X., Xie, X., Li, Z., Li, M., and Ma, W. 2005. Photo-to-search: Using multimodal queries to search the Web from mobile devices. In Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval. ACM, 143--150.
[20]
Fischler, M. and Bolles, R. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6, 381--395.
[21]
Gionis, A., Indyk, P., and Motwani, R. 1999. Similarity search in high dimensions via hashing. In Proceedings of the International Conference on Very Large Data Bases. 518--529.
[22]
Gordo, A. and Perronnin, F. 2011. Asymmetric distances for binary embeddings. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 729--736.
[23]
Harris, C. and Stephens, M. 1988. A combined corner and edge detector. In Proceedings of the Alvey Vision Conference. Vol. 15, 50.
[24]
Hays, J. and Efros, A. 2007. Scene completion using millions of photographs. ACM Trans. Graph. 26.
[25]
Hays, J. and Efros, A. 2008. IM2GPS: Estimating geographic information from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1--8.
[26]
He, J., Chang, S., Radhakrishnan, R., and Bauer, C. 2011. Compact hashing with joint optimization of search accuracy and time. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 753--760.
[27]
Hua, G., Brown, M., and Winder, S. 2007. Discriminant embedding for local image descriptors. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, 1--8.
[28]
Jain, R. 1993. NSF workshop on visual information management systems. SIGMOD Record 22, 3, 57--75.
[29]
Jing, F., Wang, C., Yao, Y., Deng, K., Zhang, L., and Ma, W. 2006. Igroup: Web image search results clustering. In Proceedings of the 14th Annual ACM International Conference on Multimedia. ACM, 377--384.
[30]
Jing, Y., Rowley, H., Rosenberg, C., Wang, J., and Covell, M. 2009. Visualizing Web Images via Google image swirl. In Proceedings of the NIPS Workshop on Statistical Machine Learning for Visual Analytics.
[31]
Jing, Y., Rowley, H., Wang, J., Tsai, D., Rosenberg, C., and Covell, M. 2012. Google image swirl: A large-scale content-based image visualization system. In Proceedings of the International World Wide Web Conference. ACM, 539--540.
[32]
Kadir, T., Zisserman, A., and Brady, M. 2004. An affine invariant salient region detector. In Proceedings of the 8th European Conference on Computer Vision. 228--241.
[33]
Ke, Y. and Sukthankar, R. 2004. PCA-sift: A more distinctive representation for local image descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vol. 2. IEEE, 506.
[34]
Koenderink, J. 1984. The structure of images. Biol. Cybernet. 50, 5, 363--370.
[35]
Le, Q., Monga, R., Devin, M., Corrado, G., Chen, K., Ranzato, M., Dean, J., and Ng, A. 2012. Building high-level features using large scale unsupervised learning. In Proceedings of the 29th International Conference on Machine Learning.
[36]
Lew, M., Sebe, N., Djeraba, C., and Jain, R. 2006. Content-based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimedia Comput. Commun. Appl. 2, 1, 1--19.
[37]
Li, B., Xiao, R., Li, Z., Cai, R., Lu, B., and Zhang, L. 2011. Rank-sift: Learning to rank repeatable local interest points. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1737--1744.
[38]
Li, J. and Allinson, N. 2008. A comprehensive review of current local features for computer vision. Neurocomputing 71, 10, 1771--1787.
[39]
Li, X., Chen, L., Zhang, L., Lin, F., and Ma, W. 2006. Image annotation by large-scale content-based image retrieval. In Proceedings of the 14th Annual ACM International Conference on Multimedia. ACM, 607--610.
[40]
Li, Z., Xie, X., Zhang, L., and Ma, W.-Y. 2007. Searching one billion Web images by content: Challenges and opportunities. In Proceedings of the International Workshop Multimedia Content Analysis and Mining (MCAM). 33--36.
[41]
Lindeberg, T. 1994. Scale-space theory: A basic tool for analyzing structures at different scales. J. Appl. Stat. 21, 1--2, 225--270.
[42]
Lindeberg, T. 1998. Feature detection with automatic scale selection. Int. J. Comput. Vision 30, 2, 79--116.
[43]
Liu, C., Yuen, J., and Torralba, A. 2011. Sift flow: Dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Machine Intell. 33, 5, 978--994.
[44]
Liu, T.-Y. 2009. Learning to rank for information retrieval. Found. Trends Info. Retriev. 3, 3, 225--331.
[45]
Lowe, D. 1999. Object recognition from local scale-invariant features. In Proceedings of the IEEE International Conference on Computer Vision. Vol. 2, IEEE, 1150--1157.
[46]
Ma, W. and Manjunath, B. 1997. Netra: A toolbox for navigating large image databases. In Proceedings of the International Conference on Image Processing. Vol. 1, IEEE, 568--571.
[47]
Matas, J., Chum, O., Urban, M., and Pajdla, T. 2004. Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput. 22, 10, 761--767.
[48]
Mikolajczyk, K. and Schmid, C. 2004. Scale & affine invariant interest point detectors. Int. J. Comput. Vision 60, 1, 63--86.
[49]
Mikolajczyk, K. and Schmid, C. 2005. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Intell. 27, 10, 1615--1630.
[50]
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Gool, L. 2005. A comparison of affine region detectors. Int. J. Comput. Vision 65, 1, 43--72.
[51]
Miller, G. 1995. Wordnet: A lexical database for english. Commun. ACM 38, 11, 39--41.
[52]
Moravec, H. 1981. Rover visual obstacle avoidance. In Proceedings of the International Joint Conference on Artificial Intelligence. 785--790.
[53]
Nister, D. and Stewenius, H. 2006. Scalable recognition with a vocabulary tree. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vol. 2. IEEE, 2161--2168.
[54]
Oliva, A. and Torralba, A. 2001. Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vision 42, 3, 145--175.
[55]
Olston, C. and Najork, M. 2010. Web crawling. Found. Trends Inf. Retriev. 4, 3, 175--246.
[56]
Pentland, A., Picard, R., and Sclaroff, S. 1994. Photobook: Content-based manipulation of image databases. In Proceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases. Vol. 2185, SPIE.
[57]
Philbin, J., Chum, O., Isard, M., Sivic, J., and Zisserman, A. 2007. Object retrieval with large vocabularies and fast spatial matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1--8.
[58]
Quack, T., Mönich, U., Thiele, L., and Manjunath, B. 2004. Cortina: A system for large-scale, content-based Web image retrieval. In Proceedings of the 12th Annual ACM International Conference on Multimedia. ACM, 508--511.
[59]
Robinson, J. 1981. The KDB-tree: A search structure for large multidimensional dynamic indexes. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 10--18.
[60]
Rosten, E. and Drummond, T. 2006. Machine learning for high-speed corner detection. In Proceedings of the 9th European Conference on Computer Vision. 430--443.
[61]
Rosten, E., Porter, R., and Drummond, T. 2010. Faster and better: A machine learning approach to corner detection. IEEE Trans. Pattern Anal. Machine Intell. 32, 1, 105--119.
[62]
Rui, Y., Huang, T., and Chang, S. 1999. Image retrieval: Current techniques, promising directions, and open issues. J. Visual Commun. image Represen. 10, 1, 39--62.
[63]
Rui, Y., Huang, T., Ortega, M., and Mehrotra, S. 1998. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans. Circuits Syst. Video Technol. 8, 5, 644--655.
[64]
Shi, S., Zhang, H., Yuan, X., and Wen, J. 2010. Corpus-based semantic class mining: Distributional vs. pattern-based approaches. In Proceedings of the 23rd International Conference on Computational Linguistics. ACL, 993--1001.
[65]
Shrivastava, A., Malisiewicz, T., Gupta, A., and Efros, A. 2011. Data-driven visual similarity for cross-domain image matching. ACM Trans. Graph. 30, 6.
[66]
Sivic, J. and Zisserman, A. 2003. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, 1470--1477.
[67]
Smeulders, A., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Machine Intell. 22, 12, 1349--1380.
[68]
Smith, J. and Chang, S. 1996. Searching for images and videos on the World-Wide Web. IEEE Multimedia Mag.
[69]
Smith, J. and Chang, S. 1997. Visualseek: A fully automated content-based image query system. In Proceedings of the 4th ACM International Conference on Multimedia. ACM, 87--98.
[70]
Sun, Z., Wang, C., Zhang, L., and Zhang, L. 2012. Query-adaptive shape topic mining for hand-drawn sketch recognition. In Proceedings of the 20th ACM International Conference on Multimedia. ACM.
[71]
Torralba, A., Fergus, R., and Freeman, W. 2008a. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Trans. Pattern Anal. Machine Intell. 30, 11, 1958--1970.
[72]
Torralba, A., Fergus, R., and Weiss, Y. 2008b. Small codes and large image databases for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1--8.
[73]
Torralba, A., Murphy, K., Freeman, W., and Rubin, M. 2003. Context-based vision system for place and object recognition. In Proceedings of IEEE International Conference on Computer Vision. IEEE, 273--280.
[74]
Trappenberg, T. P. 2010. Fundamentals of Computational Neuroscience. Oxford University Press.
[75]
Tsai, D., Jing, Y., Liu, Y., Rowley, H., Ioffe, S., and Rehg, J. 2011. Large-scale image annotation using visual synset. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, 611--618.
[76]
Tuytelaars, T. and Mikolajczyk, K. 2008. Local invariant feature detectors: A survey. Found. Trends Comput. Graphics Vision 3, 3, 177--280.
[77]
Tuytelaars, T. and Van Gool, L. 2004. Matching widely separated views based on affine invariant regions. Int. J. Comput. Vision 59, 1, 61--85.
[78]
Von Ahn, L. and Dabbish, L. 2004. Labeling images with a computer game. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 319--326.
[79]
Von Ahn, L., Liu, R., and Blum, M. 2006. Peekaboom: A game for locating objects in images. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 55--64.
[80]
Wang, B., Li, Z., Li, M., and Ma, W. 2006a. Large-scale duplicate detection for Web image search. In Proceedings of the IEEE International Conference on Multimedia and Expo. IEEE, 353--356.
[81]
Wang, J. and Hua, X. 2011. Interactive image search by color map. ACM Trans. Intell. Syst. Technol. 3, 1.
[82]
Wang, J., Kumar, S., and Chang, S. 2010a. Semi-supervised hashing for large scale search. IEEE Trans. Pattern Anal. Machine Intell. 6, 1, 1.
[83]
Wang, J., Li, J., and Wiederhold, G. 2001. Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Machine Intell. 23, 9, 947--963.
[84]
Wang, X., Zhang, L., Jing, F., and Ma, W. 2006b. Annosearch: Image auto-annotation by search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Vol. 2, IEEE, 1483--1490.
[85]
Wang, X., Zhang, L., Liu, M., Li, Y., and Ma, W. 2010b. Arista-image search to annotation on billions of Web photos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2987--2994.
[86]
Wang, X.-J., Xu, Z., Zhang, L., Liu, C., and Rui, Y. 2012. Towards indexing representative images on the web. In Proceedings of the 20th ACM International Conference on Multimedia. ACM.
[87]
Weber, R., Schek, H., and Blott, S. 1998. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In Proceedings of the International Conference on Very Large Data Bases. 194--205.
[88]
Weiss, Y., Torralba, A., and Fergus, R. 2008. Spectral hashing. In Proceedings of the Conference on Neural Information Processing Systems.
[89]
Wen, J.-R. 2009. Encyclopedia of Data Warehousing and Mining 2nd Ed. IGI Global, Chapter Enhancing Web Search through Query Log Mining, 758--763.
[90]
Witkin, A. 1983. Scale-space filtering. In Proceedings of the 8th International Joint Conference on Artificial Intelligence. Vol. 2, Morgan Kaufmann Publishers Inc., 1019--1022.
[91]
Wu, Z., Ke, Q., Isard, M., and Sun, J. 2009. Bundling features for large scale partial-duplicate Web image search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 25--32.
[92]
Zha, Z., Yang, L., Mei, T., Wang, M., and Wang, Z. 2009. Visual query suggestion. In Proceedings of the 17th ACM International Conference on Multimedia. ACM, 15--24.
[93]
Zhang, S., Huang, Q., Hua, G., Jiang, S., Gao, W., and Tian, Q. 2010. Building contextual visual vocabulary for large-scale image applications. In Proceedings of the International Conference on Multimedia. ACM, 501--510.
[94]
Zhang, S., Tian, Q., Hua, G., Huang, Q., and Li, S. 2009a. Descriptive visual words and visual phrases for image applications. In Proceedings of the 17th ACM International Conference on Multimedia. ACM, 75--84.
[95]
Zhang, X., Li, Z., Zhang, L., Ma, W., and Shum, H. 2009b. Efficient indexing for large scale visual search. In Proceedings of the IEEE International Conference on Computer Vision. IEEE, 1103--1110.
[96]
Zhang, X., Zhang, L., and Shum, H. 2012a. Qsrank: Query-sensitive hash code ranking for efficient-neighbor search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2058--2065.
[97]
Zhang, X., Zhang, L., Wang, X.-J., and Shum, H.-Y. 2012b. Finding celebrities in billions of Web images. IEEE Trans. Multimedia 14, 4, 995--1007.
[98]
Zhang, Y., Jia, Z., and Chen, T. 2011. Image retrieval with geometry-preserving visual phrases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 809--816.
[99]
Zheng, Y., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T., and Neven, H. 2009. Tour the world: Building a Web-scale landmark recognition engine. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1085--1092.
[100]
Zhou, X. and Huang, T. 2003. Relevance feedback in image retrieval: A comprehensive review. Multimedia Syst. 8, 6, 536--544.

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 9, Issue 1s
Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
October 2013
218 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2523001
Issue’s Table of Contents
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Publication History

Published: 17 October 2013
Accepted: 01 March 2013
Revised: 01 March 2013
Received: 01 September 2012
Published in TOMM Volume 9, Issue 1s

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Author Tags

  1. Review
  2. Web image search
  3. big data
  4. content-based
  5. global feature
  6. image feature
  7. image knowledge base
  8. image retrieval
  9. indexing
  10. local feature
  11. visual representation

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