By Jinhee Chun, Natsuda Kaothanthong (auth.), Richard Wilson, Edwin Hancock, Adrian Bors, William Smith (eds.)
ISBN-10: 3642402453
ISBN-13: 9783642402456
ISBN-10: 3642402461
ISBN-13: 9783642402463
The quantity set LNCS 8047 and 8048 constitutes the refereed complaints of the fifteenth overseas convention on computing device research of pictures and styles, CAIP 2013, held in York, united kingdom, in August 2013. The 142 papers offered have been conscientiously reviewed and chosen from 243 submissions. The scope of the convention spans the next components: 3D television, biometrics, colour and texture, rfile research, graph-based equipment, picture and video indexing and database retrieval, photo and video processing, image-based modeling, kernel tools, clinical imaging, cellular multimedia, model-based imaginative and prescient methods, movement research, typical computation for electronic imagery, segmentation and grouping, and form illustration and analysis.
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Extra resources for Computer Analysis of Images and Patterns: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II
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Et al. ) ISVC 2008, Part I. LNCS, vol. 5358, pp. 11–22. Springer, Heidelberg (2008) Supporting Ancient Coin Classification 25 9. : Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 91–110 (2004) 10. : Coarse-to-Fine Correspondence Search for Classifying Ancient Coins. , Kim, J. ) ACCV Workshops 2012, Part II. LNCS, vol. 7729, pp. 25–36. Springer, Heidelberg (2013) 11. : Automatic attribution of ancient Roman imperial coins. In: CVPR, pp. 1728–1734 (2010) 12.
6807 Comp. 942 Experimental Results We compare our method with the closest previously proposed algorithm [9], and [4] (reported the best performance on CUHK database), as well as [7], on both of artistic (CUHK) and non-artistic sketches. 9, from Caucasian, Indian, East Asian, and Middle Eastern races). Table 1 illustrates the improvement due to the use general-specific modeling compared with direct comparison (original), specific, and general models. We also show that better results can be achieved by piecewise rather than holistic comparison (total sum of differences).
1. Dense features are extracted from an image set. 2. The extracted features are quantized to build a visual vocabulary of size M . The most common method used for quantization is k-means clustering where k is number of cluster centers equal to M . 3. To represent a novel image using the visual vocabulary, dense features are extracted from it and mapped to the visual vocabulary. The mapping is based on Euclidean distance between a word and a given descriptor. 4. Finally, a histogram of visual words is built to represent the novel image.
Computer Analysis of Images and Patterns: 15th International Conference, CAIP 2013, York, UK, August 27-29, 2013, Proceedings, Part II by Jinhee Chun, Natsuda Kaothanthong (auth.), Richard Wilson, Edwin Hancock, Adrian Bors, William Smith (eds.)
by Christopher
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