Abstract
Traditional image retrieval technology is pixel sensitive and with low fault tolerance. To overcome this deficiency, a novel method for large-scale image retrieval is proposed in this paper, which is especially suitable for images with kinds of interferences, such as rotation, pixel lost, watermarks, etc. First, local features of images are extracted to build a visual dictionary with weight, which is a new data structure developed from bag-of-words. In the retrieval process, we look up all the features extracted from the target image in the dictionary and create a single list of weight to get the result. We demonstrate the effectiveness of our approach using a coral image set and online image set on eBay.
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References
Farquhar, J., Szedmak, S., Meng, H., Shawe-Taylor, J.: Improving “Bag-of-keypoints” image categorization. University of Southampton (2005)
Jegou, H., Douze, M., Schmid, C.: On the Burstiness of Visual Elements. In: IEEE Conference on Computer Version and Recognition, Miami, FL, USA, pp. 1169–1176 (2009)
Edward, R., Drummond, T.: Fusing Points and Lines for High Performance Tracking. In: ICCV, pp. 1508–1515 (2005)
Edward, R., Drummond, T.: Machine Learning for High-speed Corner Detection. In: Machine Learning, pp. 1–14 (2006)
Lowe, D.: Object Recognition from Scale-invariant Features. In: ICCV, pp. 1150–1157 (1999)
Leutenegger, S., Margarita, C., Roland, Y.: Siegwart. BRISK: Binary Robust Invariant Scalable Keypoints. In: ICCV (2011)
Anna, B., Andrew, Z., Xavier, M.: Image Classification using Random Forests and Ferns. In: ICCV, pp. 1–8 (2007)
Ethan, R., Vincent, R., Kurt, K., Gary, B.: ORB: an Efficient Alternative to SIFT or SURF. In: ICCV (2011)
Muja, M., Lowe, D.: Fast Approximate Nearest Neighbours with Automatic Algorithm Configuration. In: 4th International Conference on Computer Vision Theory and Applications, pp. 331–340. Springer, France (2009)
Csurka, G., Bray, C., Dance, C., Fan, L.: Visual Categorization with Bags of Keypoints. In: Proc. of ECCV Workshop on Statistical Learning in Computer Vision. Czech Republic, Prague (2004)
Kekre, H.B., Dhirendra, M.: Digital Image Search & Retrieval using FFT Sectors of Color Images. International Journal on Computer Science and Engineering, 368–372 (2010)
Bian, W., Tao, D.C.: Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval. IEEE Transactions on Image Processing, 545–554 (2010)
Perronnin, F., Liu, Y., Sanchez, J.: Large-scale Image Retrieval with Compressed Fisher Vectors. In: Computer Vision and Pattern Recognition (CVPR), pp. 3384–3391 (2010)
Douze, M., Ramisa, A., Schmid, C.: Combining Attributes and Fisher Vectors for Efficient Image Retrieval. In: Computer Vision and Pattern Recognition (CVPR), pp. 754–752 (2011)
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Yin, CQ., Mao, W., Jiang, W. (2012). Very Large-Scale Image Retrieval Based on Local Features. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_36
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DOI: https://doi.org/10.1007/978-3-642-31837-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31836-8
Online ISBN: 978-3-642-31837-5
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