Abstract
Derive from practical needs, especially in tourism industry; landmark recognition is an interesting and challenging problem on mobile devices. To obtain the robustness, landmarks are described by local features with many levels of invariance among which rotation invariance is commonly considered an important property. We propose to eliminate orientation normalization for local visual descriptors to enhance the accuracy in landmark recognition problem. Our experiments show that with three different widely used descriptors, including SIFT, SURF, and BRISK, our idea can improve the recognition accuracy from 2.3 to 12.6% while reduce the feature extraction time from 2.5 to 11.1%. This suggests a simple yet efficient method to boost the accuracy with different local descriptors with orientation normalization in landmark recognition applications.
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References
Ahmad, S., Eskicioglu, R., Graham, P.: Design and Implementation of a Sensor Network Based Location Determination Service for use in Home Networks. In: IEEE International Conference on Mobile Ad Hoc and Sensor Systems, pp. 622–626 (2006)
Sundaramurthy, M.C., Chayapathy, S.N., Kumar, A., Akopian, D.: Wi-Fi assistance to SUPL-based Assisted-GPS simulators for indoor positioning. In: Consumer Communications and Networking Conference (CCNC), pp. 918–922 (2011)
Hsu, C.-H., Yu, C.-H.: An Accelerometer Based Approach for Indoor Localization. In: Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, pp. 223–227 (2009)
Jarng, S.: HMM Voice Recognition Algorithm Coding. In: International Conference on Information Science and Applications, pp. 1–7 (2011)
Adamek, T., Marimon, D.: Large-scale visual search based on voting in reduced pose space with application to mobile search and video collections. In: IEEE International Conference on Multimedia and Expo, pp. 1–4 (2011)
Wilhelm, M.: A generic context aware gesture recognition framework for smart environments. In: International Conference on Pervasive Computing and Communications Workshops, pp. 536–537 (2012)
Devendran, V., Thiagarajan, H., Wahi, A.: SVM Based Hybrid Moment Features for Natural Scene Categorization. In: International Conference on Computational Science and Engineering, pp. 356–361 (2009)
Dai-Duong, T., Chau-Sang, N., Vinh-Tiep, N., Minh-Triet, T., Anh-Duc, D.: Realtime arbitrary-shaped template matching process. In: 12th International Conference on Control Automation, Robotics and Vision, pp. 1407–1412 (2012)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA (2010)
Chen, T., Li, Z., Yap, K.-H., Wu, K., Chau, L.-P.: A multi-scale learning approach for landmark recognition using mobile devices. In: 7th International Conference on Information, Communications and Signal Processing, ICICS 2009, Macau (2009)
Bandera, A., Marfil, R., Vzquez-Martn, R.: Incremental Learning of Visual Landmarks for Mobile Robotics. In: 20th International Conference on Pattern Recognition (ICPR), Istanbul (2010)
Lee, L.-K., An, S.-Y., Oh, S.-Y.: Efficient visual salient object landmark extraction and recognition. In: International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, AK (2011)
Zhang, J., Marszalek, M., Lazabnik, S.: Local features and kernels for classification of texture and object categories: A comprehensive study. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006 (2006)
Fei-Fei, L., Perona, P.: A Bayesian Hierarchical Model for Learning Natural Scene Categories. In: Computer Vision and Pattern Recognition, pp. 524–531 (2005)
The Oxford Buildings Dataset, http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/
The Paris Dataset, http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/
Shin, C., Kim, H., Kang, C., Jang, Y., Choi, A., Woo, W.: Unified Context-Aware Augmented Reality Application Framework for User-Driven Tour Guides. In: International Symposium on Ubiquitous Virtual Reality (ISUVR), Gwangju (2010)
Chen, D., Tsai, S., Hsu, C.-H., Singh, J. P., Girod, B.: Mobile augmented reality for books on a shelf. International Conference on Multimedia and Expo (ICME), Barcelona (2011)
Nistér, D., Stewénius, H.: Linear Time Maximally Stable Extremal Regions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 183–196. Springer, Heidelberg (2008)
Rosten, E., Porter, R., Drummond, T.: Faster and Better: A Machine Learning Approach to Corner Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1), 105–119 (2010)
Chandrasekhar, V., Takacs, G., Chen, D., Tsai, S., Grzeszczuk, R., Girod, B.: CHoG: Compressed histogram of gradients A low bit-rate feature descriptor. In: Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL (2009)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Computer Vision and Pattern Recognition, CVPR 2006 (2006)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.: SURF: Speeded Up Robust Features. In: Computer Vision and Image Understanding (CVIU), pp. 346–359 (2008)
Baatz, G., Köser, K., Chen, D., Grzeszczuk, R., Pollefeys, M.: Handling Urban Location Recognition as a 2D Homothetic Problem. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 266–279. Springer, Heidelberg (2010)
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Truong, DD., Ngoc, CS.N., Nguyen, VT., Tran, MT., Duong, AD. (2014). Local Descriptors without Orientation Normalization to Enhance Landmark Regconition. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_34
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DOI: https://doi.org/10.1007/978-3-319-02741-8_34
Publisher Name: Springer, Cham
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