Beyond local phase quantization: Mid-level blurred image representation using fisher vector

Blurred image recognition is still remaining as a challenging task, while with the wide applications. One principal way for solving this problem is to extract the blur-invariant visual descriptor. To this end, local phase quantization (LPQ) was ever proposed, and achieved promising results. In this paper, to further enhance LPQ´s performance, we propose to apply Fisher Vector (FV) encoding approach to acquire the mid-level blurred image representation. To our knowledge, it is the first time that the descriptive power of FV for blurred image recognition has been investigated. Instead of being extracted holistically from the whole image as previously, LPQ is acquired in a densely sampled way. That is, a sliding sub-window will screen the image with certain vertical and horizontal strides. LPQs are then extracted from all the resulting sub-windows respectively. In addition, to maintain local spatial structure information, each sub-window will be divided into finer cells. After being FV encoded, the local LPQs are aggregated using sum-pooling to generate the image signature. The experimental results on three datasets demonstrate that FV can enhance LPQ´s performance significantly, and our proposition also outperforms the other blur-invariant descriptors by large margins in most cases.