The Application of KAZE features to the classification echocardiogram videos

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9059, Issue , 2015, Pages 61-72

  Li, Wei ^a   Qian, Yu ^a   Loomes, Martin ^a   Gao, Xiaohong ^a  

^a MIDDLESEX UNIVERSITY   (United Kingdom)

Author keywords

3D SIFT; Bag of words; Classification of echocardiogram videos; Fisher vector; KAZE; Sparse coding; SURF; SVM

Indexed keywords

CODES (SYMBOLS); COMPUTER VISION; IMAGE CODING; MEDICAL IMAGING;

3D SIFT; BAG OF WORDS; FISHER VECTORS; KAZE; SPARSE CODING; SURF; SVM;

CLASSIFICATION (OF INFORMATION);

EID: 84952766202     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24471-6_6     Document Type: Conference Paper

References (23)

1. Kumar, R., Wang, F., Beymer, D., Syeda-Mahmood, T.: Cardiac disease detection from echocardiogram using edge filtered scale-invariant motion features. In: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA (2010)
2. Takeshima, S., Matsuda, H., Yoshinaga, T., Masuda, K.: Development of automatic recognition software of left ventricle by time series processing echocardiograms and application to disease heart. In: Biomedical Engineering International Conference, BMEI (2011)
3. Ebadollahi, S., Chang, S.-F., Wu, H.: Automatic view recognition in echocardiogram videos using parts-based representation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2-9 (2004)
4. Zhou, S.K., Park, J.H., Georgescu, B., Simopoulos, C., Otsuki, J., Comaniciu, D.: Imagebased multiclass boosting and echocardiographic vies classification. In: IEEE Conference on Computer Vision and Pattern Recognition (2006)
5. Kumar, R., Wang, F., Beymer, D., Syeda-Mahmood, T.: Echocardiogram view classification using edge filtered scale-invariant motion features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.723-730 (2009)
6. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91-110 (2004)
7. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features(SURF). Comput. Vis. Image Underst. 110, 346-359 (2008)
8. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE Conference Computer Vision and Pattern Recognition, CVPR, pp. 506-513 (2004)
9. Morel, J.M., Guoshen, Y.: ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2, 438-469 (2009)
10. 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)
11. Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 214-227. Springer, Heidelberg (2012)
12. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629-639 (1990)
13. Weickert, J., ter Haar Romeny, B.M., Viergever, M.A.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7(3), 398-410 (1998)
14. Ter Haar Romeny, B.M.: Front-End Vision And Multi-Scale Image Analysis. Kluwer Academic, Dordrecht (2003)
15. Weickert, J.: Efficient image segmentation using partial differential equations and morphology. Pattern Recogn. 34, 1813-1824 (2001)
16. Brown, M., Lowe, D.: Invariant features from interest point groups. In: British Machine Vision Conference, BMVC, Cardiff, UK (2002)
17. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143-156. Springer, Heidelberg (2010)
18. Revaud, J., Douze, M., Schmid, C., Jégou, H.: Event retrieval in large video collections with circulant temporal encoding. In: IEEE Conference on Computer Vision and Patern Recognision (CVPR), pp. 2459-2464 (2013)
19. Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1704-1716 (2012)
20. Kantorov, V., Laptev, I.: Efficient feature extraction, encoding and classification for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2593-2600 (2014)
21. Qian, Y., Wang, L., Wang, C., Gao, X.: The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 68-79. Springer, Heidelberg (2013)
22. Cai, T.T., Lei, W.: Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory 57(7), 4680-4688 (2011)
23. Fisher Vector. www.vlfeat.org/api/fisher.html