Video similarity search by using compact representations

The amount of applications using unstructured data, like videos, has been increased, and the researches concerning multimedia retrieval have attracted great attention. The need to efficiently index and retrieve this kind of data is of great concern, due to the fact that common searching approaches based on the use of keywords are not adequate for large video databases. Similarity search is a content based approach and it has been successfully used in retrieval systems. Accordingly, a major challenge is to provide an accurate and compact video representation that can achieve good performance with a fast answer in this type of searching. In this work, we proposed a compact video representation by using Min-Hash and the k-nearest GIST descriptors. Furthermore, we also present the first use of BossaNova Video Descriptor (BNVD) to video similarity search. Both compact video representations have shown more than 88% of mean average precision on similarity video search. The experimental results indicate high efficiency of our proposed representations in video retrieval task.