Fisher vectors (FV) aggregated from local invariant features (e.g., SIFT) is one of the state-of-the-art descriptors for visual search, due to high discriminability but small visual vocabulary. Nevertheless, a high-dimensional FV needs to be compressed into a compact descriptor for light storage and high matching eficiency. In this paper, we formulate the FV compression as a resource-constrained optimization problem. Our goal is to maximize search performance subject to the constraints of descriptor compactness, compression complexity in terms of memory usage and time cost. Accordingly, we present a selective binary Fisher codes (SBFC) to compress the raw FV. Firstly, to fulfill the constraint of compression complexity, we binarize the FV by a sign function, Secondly, we propose to select discriminative bits from the binarized FV (BFC) to maximize search performance, subject to the constraint of descriptor compactness. Extensive experiments over MPEG Compact Descriptor for Visual Search (CDVS) benchmark datasets have shown that S-BFC significantly improves search performance at a smaller descriptor size as well as much lower complexity, compared with the state-of-the-art FV compression algorithms like Hashing and Product Quantziation (PQ). A simplified version of SBFC, SBFC LS has been adopted by the MPEG CDVS standard. In the CDVS evaluation framework, SBFC LS has achieved promising performance mean Average Precision (mAP) 83% on average at much lower memory cost of 40KB.
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