Local phase quantization plus: A principled method for embedding local phase quantization into Fisher vector for blurred image recognition

摘要

Highlights ? LPQ + is proposed as a novel blur-insensitive visual descriptor. ? Fisher vector is employed to leverage the performance of blurred image recognition. ? LPQ + quantizes the local STFT phases directly. ? Compared to LPQ, LPQ + takes the advantages of effectiveness and efficiency. Abstract Advances in computer vision and image processing technology have led to great success in image recognition when the images are clear. However, in real-world applications, images are often blurred due to factors such as atmospheric turbulence, object-camera relative motion, and focus. This imposes a great challenge on practical image recognition tasks. To improve the performance of burred image recognition, one main approach is to extract blur-insensitive visual descriptors. A well-established blur-insensitive texture feature, local phase quantization (LPQ), can achieve promising results, with a trade-off between effectiveness and efficiency. However, for complicated visual recognition tasks, its performance is still not satisfactory. To leverage the discriminative power of LPQ, we propose local phase quantization plus ( LPQ + ), which embeds LPQ into Fisher vector (FV) to acquire mid-level blurred image representation under the bag-of-words (BoW)