Cao J. Representation and Recognition of the Image Target. Beijing, China: China Machine Press, 2012 (in Chinese)(曹 健.图像目标的表示与识别.北京:机械工业出版社, 2012)
[2]
Sutherland N S. Object Recognition // Edward C, Carterette M P F, eds. China Handbook of Perception: Biology of Perceptual Systems. New York, USA: Elsevier, 2012, 3: 157-185
[3]
Deng J, Berg A C, Li K, et al. What Does Classifying More than 10000 Image Categories Tell Us? // Proc of the 11th European Conference on Computer Vision. Heraklion, Greece, 2010: 71-84
[4]
Fang S W, Qu Y Y, Chen C, et al. Object Localization Based on Discriminative Visual Words // Proc of the International Conference on Machine Learning and Cybernetics. Xi'an, China, 2012, III: 1111-1117
[5]
Guillaumin M, Ferrari V. Large-Scale Knowledge Transfer for Object Localization in ImageNet // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 3202-3209
[6]
Lampert C H, Blaschko M B, Hofmann T. Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2129-2142
[7]
Wojek C, Dorkó G, Schulz A, et al. Sliding-Windows for Rapid Object Class Localization: A Parallel Technique // Proc of the 30th DAGM Symposium on Pattern Recognition. Munich, Germany, 2008: 71-81
[8]
Gould S, Rodgers J, Cohen D, et al. Multi-class Segmentation with Relative Location Prior. International Journal of Computer Vision, 2008, 80(3): 300-316
[9]
Singaraju D, Vidal R. Using Global Bag of Features Models in Random Fields for Joint Categorization and Segmentation of Objects // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 2313-2319
[10]
Russell B C, Freeman W T, Efros A A, et al. Using Multiple Segmentations to Discover Objects and Their Extent in Image Collections // Proc of the IEEE Computer Society Conference on Compu-ter Vision and Pattern Recognition. New York, USA, 2006, II: 1605-1614
[11]
Malisiewicz T, Efros A A. Recognition by Association via Learning Per-exemplar Distances // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA, 2008. DOI: 10.1109/CVPR.2008.4587462
[12]
Shi J B, Malik J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905
[13]
Sivic J, Russell B C, Efros A A, et al. Discovering Objects and Their Location in Images // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005, I: 370-377
[14]
Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[15]
Borgelt C. Efficient Implementations of Apriori and Eclat // Proc of the 1st IEEE ICDM Workshop on Frequent Item Set Mining Implementations. Melbourne, USA, 2003: 90
[16]
Cheng H, Yan X F, Han J W, et al. Discriminative Frequent Pa-ttern Analysis for Effective Classification // Proc of the 23rd IEEE International Conference on Data Engineering. Istanbul, Turkey, 2007: 716-725
[17]
Kullback S, Leibler R A. On Information and Sufficiency. The Annals of Mathematical Statistics, 1951, 22(1): 79-86
[18]
Boughorbel S, Tarel J P, Boujemaa N. Generalized Histogram Intersection Kernel for Image Recognition // Proc of the IEEE International Conference on Image Processing. Genova, Italy, 2005, III: 161-164
[19]
Fulkerson B, Vedaldi A, Soatto S. Class Segmentation and Object Localization with Superpixel Neighborhoods // Proc of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan, 2009: 670-677
[20]
Dellaert F, Frahm J M, Pollefeys M, et al. Outdoor and Large-Scale Real-World Scene Analysis. Berlin, Germany: Springer, 2012
[21]
Liang J Z, Corso N, Turner E, et al. Image Based Localization in Indoor Environments // Proc of the 4th International Conference on Computing for Geospatial Research and Application. San Jose, USA, 2013: 70-75
[22]
Kim S, Jin X, Han J W. DisIclass: Discriminative Frequent Pa-ttern-Based Image Classification[EB/OL]. [2014-02-24]. http://web.engr.illinois.edu/~hanj/pdf/mdm10_skim.pdf
[23]
Zhang H, Berg A C, Maire M, et al. SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA, 2006, II: 2126-2136
[24]
Han J W, Cheng H, Xin D, et al. Frequent Pattern Mining: Cu-rrent Status and Future Directions. Data Mining and Knowledge Discovery, 2007, 15(1): 55-86
[25]
Yan Y J, Li Z J, Chen H W, Frequent Item Sets Mining Algorithms. Computer Science, 2004, 31(3): 112-114, 124 (in Chinese)(颜跃进,李舟军,陈火旺.频繁项集挖掘算法.计算机科学, 2004, 31(3): 112-114, 124)