Abstract
In this work we perform an extensive comparative study of approaches for mobile visual recognition by simultaneously evaluating the performance and the computational cost of state-of-the-art key-point detection, feature extraction and encoding algorithms. Every step is independently tested so that its contribution to the final computational cost can be measured. The widely used OpenCV library is utilized for the implementation of the algorithms, while the evaluation is performed on the PASCAL VOC 2007 dataset, a challenging real world dataset crawled from the web. Our study identifies the algorithmic configurations that manage to optimally balance performance and computational cost, and provide a viable solution for real time mobile visual recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, M., Konolige, K., Blas, M.R.: Censure: Center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)
Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: Fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16-21 (2012)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Berg, D.: Apple says: Mobile application performance matters, October 29 (2012), http://www.apmdigest.com/apple-says-mobile-application-performance-matters
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000)
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)
Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: British Machine Vision Conference (2011)
Cortes, C., Vapnik, V.: Support-vector networks. In: Machine Learning, pp. 273–297 (1995)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) (2007) Results, http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012, VOC 2012 (2012) Results, http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: Liblinear: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Girod, B., Chandrasekhar, V., Chen, D.M., Cheung, N.-M., Grzeszczuk, R., Reznik, Y.A., Takacs, G., Tsai, S.S., Vedantham, R.: Mobile visual search. IEEE Signal Process. Mag. 28(4), 61–76 (2011)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. of Fourth Alvey Vision Conference, pp. 147–151 (1988)
Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 3304–3311 (June 2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)
Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 985–1002 (2008)
Liu, X., Hull, J.J., Graham, J., Moraleda, J., Bailloeul, T.: Mobile visual search, linking printed documents to digital media. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2010)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, ICCV 1999, vol. 2, pp. 1150–1157. IEEE Computer Society, Washington, DC (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput. 22(10), 761–767 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application, VISSAPP 2009, pp. 331–340. INSTICC Press (2009)
Over, P., Awad, G., Fiscus, J., Smeaton, A.F., Kraaij, W., Qunot, G.: TRECVID 2011 – An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics. In: Proceedings of TRECVID 2011. NIST, USA (December 2011)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2007)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: International Conference on Computer Vision, Barcelona (2011)
Shi, J., Tomasi, C.: Good features to track. In: 1994 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 1994, pp. 593–600 (1994)
Takacs, G., Chandrasekhar, V., Gelfand, N., Xiong, Y., Chen, W.-C., Bismpigiannis, T., Grzeszczuk, R., Pulli, K., Girod, B.: Outdoors augmented reality on mobile phone using loxel-based visual feature organization. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 427–434 (2008)
van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(1) (2008)
van Gemert, J.C., Veenman, C.J., Smeulders, A.W.M., Geusebroek, J.M.: Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(7), 1271–1283 (2010)
Vapnik, V.N.: Statistical learning theory, 1st edn. Wiley (1998)
Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., Schmalstieg, D.: Pose tracking from natural features on mobile phones. In: Proceedings of the 7th International Symposium on Mixed and Augmented Reality (2008)
Zhou, X., Yu, K., Zhang, T., Huang, T.S.: Image classification using super-vector coding of local image descriptors. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 141–154. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chatzilari, E., Liaros, G., Nikolopoulos, S., Kompatsiaris, Y. (2013). A Comparative Study on Mobile Visual Recognition. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-39712-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39711-0
Online ISBN: 978-3-642-39712-7
eBook Packages: Computer ScienceComputer Science (R0)