A compact binary aggregated descriptor via dual selection for visual search

To achieve high retrieval accuracy over a large scale image/video dataset, recent research efforts have demonstrated that employing extremely high-dimensional descriptors such as the Fisher Vector (FV) and the Vector of Locally Aggregated Descriptors (VLAD) can yield good performance. To enable fast search, the FV (or VLAD) is usually compressed by product quantization (PQ) or hashing. However, compressing high-dimensional descriptors via PQ or hashing may become intractable and infeasible due to both the storage and computation requirements for the linear/nonlinear projection of PQ or hashing methods. We develop a novel compact aggregated descriptor via dual selection for visual search. We utilize both sample-specific Gaussian component redundancy and bit dependency within a binary aggregated descriptor to produce its compact binary codes. The proposed method can effectively reduce the codesize of the raw aggregated descriptors, without degrading the search accuracy or introducing additional memory footprint. We demonstrate the significant advantages of the proposed binary codes in solving the approximate nearest neighbor (ANN) visual search problem. Experimental results on extensive datasets show that our method outperforms the state-of-the-art methods.