  Image classification is an important task in the field of machine learning
and image processing. However, the usually used classification method --- the K
Nearest-Neighbor algorithm has high complexity, because its two main processes:
similarity computing and searching are time-consuming. Especially in the era of
big data, the problem is prominent when the amount of images to be classified
is large. In this paper, we try to use the powerful parallel computing ability
of quantum computers to optimize the efficiency of image classification. The
scheme is based on quantum K Nearest-Neighbor algorithm. Firstly, the feature
vectors of images are extracted on classical computers. Then the feature
vectors are inputted into a quantum superposition state, which is used to
achieve parallel computing of similarity. Next, the quantum minimum search
algorithm is used to speed up searching process for similarity. Finally, the
image is classified by quantum measurement. The complexity of the quantum
algorithm is only O((kM)^(1/2)), which is superior to the classical algorithms.
Moreover, the measurement step is executed only once to ensure the validity of
the scheme. The experimental results show that, the classification accuracy is
83.1% on Graz-01 dataset and 78% on Caltech-101 dataset, which is close to
existing classical algorithms. Hence, our quantum scheme has a good
classification performance while greatly improving the efficiency.
