关于codebook的不同用法，摘自[1]：

The methods differ on the details of the codebook, but more fundamentally they differ in how strictly the geometry of the configuration of parts constituting an object class is constrained.

For example, Csurka et al. [2], Bar-Hillel et al. [3] and Opelt et al. [4] simply use a "bag of visual words" model (with no geometrical relations between the parts at all), Agarwal & Roth [5], Amores et al. [6], and Vidal-Naquet and Ullman [7] use quite loose pairwise relations, whilst Fergus et al. [8] have a strongly parametrized geometric model consisting of a joint Gaussian over the centroid position of all the parts. The  approaches using no geometric relations are able to categorize images (as containing the object class), but generally do not provide location information (no detection). Whereas the methods with even loose geometry are able to detect the object's location.

从图像中提取出visual fragments之后，如何利用呢？

1. 无组织的利用：bag of visual words [2,3,4]
2. 考虑visual fragments之间的组织关系，怎么表示它们之间的关系呢，待详读[5,6,7,8]。

Referecnes:

[1] Opelt, A.; Pinz, A. & Zisserman, A. A Boundary-Fragment-Model for Object Detection European Conference on Computer Vision, 2006, 575-588

[2] G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In ECCV04. Workshop on Stat. Learning in Computer Vision, pages 59-74, 2004.

[3] A. Bar-Hillel, T. Hertz, and D. Weinshall. Object class recognition by boosting a part-based model. In Proc. CVPR, volume 2, pages 702-709, June 2005.

[4] A. Opelt, M. Fussenegger, A. Pinz, and P. Auer. Weak hypotheses and boosting for generic object detection and recognition. In Proc. ECCV, pages 71-84, 2004.

[5] S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via a sparse, part-based representation. IEEE PAMI, 26(11):1475-1490, Nov. 2004.

[6] J. Amores, N. Sebe, and P. Radeva. Fast spatial pattern discovery integrating boosting with constellations of contextual descriptors. In Proc. CVPR, volume 2, pages 769-774, CA, USA, June 2005.

[7] M. Vidal-Naquet and S. Ullman. Object recognition with informative features and linear classi¯cation. In Proc. ICCV, volume 1, pages 281-288, 2003.

[8] R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proc. CVPR, pages 264-271, 2003.