Fast content identification in high dimensional feature spaces using Sparse Ternary Codes
We consider the problem of fast content identification in high dimensional feature spaces where a sub linear search complexity is required. By formulating the problem as sparse approximation of projected coefficients, a closed form solution can be found which we approximate as a ternary representation. Hence, as opposed to dense binary codes, a framework of Sparse Ternary Codes (STC) is proposed resulting in sparse, but robust representation and sub linear complexity of search. The proposed method is compared with the Locality Sensitive Hashing (LSH) and the memory vectors on several large scale synthetic and public image databases, showing its superiority.
2016 IEEE International Workshop on Information Forensics and Security (WIFS)