Wu et al. present an unsupervised fully automatic algorithm for detecting commercials in broadcast TV. Their solution is scalable and efficient for fast, large scale, unsupervised commercial detection. Gudmund et al. revisit a cluster pruning algorithm, considering factors such as CPU/IO cost and memory constraint for large-scale copy detection. The method shows interesting clustering and retrieval computational cost when scaling up. In Kosh, processing and optimizing strategies are presented along with a cost model for integrating a similarity-based image join in a multimedia database. 
