[paper reading]"Efficient visual search of videos cast as text retrieval," J. Sivic, and A. Zisserman, IEEE TPAMI, 2009.

This paper introduce a method to search object in video using text retrieval approach. The algorithm contain two parts, first part is pre-processing. It finds affine covariant regions in each keyframes and represent it using SIFT descriptor, and then discard regions that are unstable in temporal domain. It then randomly select frames to form the visual vocabulary using the stable regions clusters. It also apply a stop-word remove method which discard visual word that appear in nearly all frames to improve the result. After remove stop-word, it calculate tf-idf weighted document frequency, and then build inverted file indexing structure. The last three steps are borrow the idea from text retrieval. The second part is run-time retrieval, when a user select a query region(in a image), it first find the visual word in the query region, and then retrieval keyframes using visual word frequency, finally it re-rank the result using spatial consistency of the visual word to further improve the ranking result. This paper doesn't use much complicate method or math, but it has a great result just using idea borrow from other field. It shows that we don't necessary have to come up with complicate idea to be success, sometimes the idea could be simple but useful. The other good things about this paper is that it has an extensive expirement on a lot of different aspect. These are really worth us to learn.