2014/10/15 by Xiankai Lu, Zheng Fang, Lu, Xiankai +7
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Remote-Sensing Image Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1410.3905
5pages,4 figures
arxiv created 2014/10/15 · openalex publication_date 2014/10/15 · arxiv updated 2014/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In object recognition, Fisher vector (FV) representation is one of the state-of-art image representations ways at the expense of dense, high dimensional features and increased computation time. A simplification of FV is attractive, so we propose Sparse Fisher vector (SFV). By incorporating locality strategy, we can accelerate the Fisher coding step in image categorization which is implemented from a collective of local descriptors. Combining with pooling step, we explore the relationship between coding step and pooling step to give a theoretical explanation about SFV. Experiments on benchmark datasets have shown that SFV leads to a speedup of several-fold of magnitude compares with FV, while maintaining the categorization performance. In addition, we demonstrate how SFV preserves the consistence in representation of similar local features.