We participate in all two types of instance search task in TRECVID 2012: automatic search and interactive search. This paper presents our approaches and results. In this task, we mainly focus on exploring the effective feature representation, feature matching, re-ranking algorithm and query expansion. In feature representation, we adopt two basic visual features and five keypoint-based BoW features, and combine them to represent effectively the frame image. In feature matching, multi-bag SVM is adopted since it can make full use of few query examples. Moreover, we conduct keypoint matching algorithm on the top ranked results. It is effective yet efficient since only top ranked results are concerned. In re-ranking stage, we observe that the top ranked videos always contain a few noisy videos. To eliminate such noise, we proposed a re-ranking algorithm based on semi-supervised learning to refine the top ranked results. In query expansion, we automatically crawl extra training images from Flickr according to the names of query instance. We achieve the good results in both tasks. Official evaluations show that our team is ranked 2nd on automatic search and 1st on interactive search.