Fast content identification in high-dimensional feature spaces using Sparse Ternary Codes

摘要

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.