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Color fundus image quality greatly influence the doctors’ diagnostic accuracy. However, the problems of imbalance data and small sample are the key issues of the color fundus images quality assessment. Hence, this paper purposes a small sample color fundus image quality assessment based on gcforest to solve these problems. Firstly, this paper extracts color and texture features to represent the quality of color fundus image. Next, re-sampling process is used to re-balance training data. Thirdly, the training data after re-balanced is sent to train gcforest which is a forest integration model. Finally, the trained gcforest which is good for small sample problem is used to evaluate color fundus images quality. Experiments demonstrate that the proposed method not only in color fundus image quality assessment but also in glaucoma classification task get good results.

Person re-identification (re-ID) is an instance-level recognition mission, which depends on specific discriminative features. However, the learned features from the network are very easily influenced and weakened by the semantic misalignment of the target and background clutters. In this work, an original deep re-ID CNN is proposed, named semantically consistent attention network (SCANet), to learn the discriminative feature. In this work, we realize the purpose by designing a foreground mask module on backbone network composed of residual block. Importantly a novel consistent attention loss is introduced to dynamically keep the inferred foreground mask similar from the shallow-level, mid-level and deep-level feature maps. As a consequence, the network will concentrate on the foreground areas at the shallow layer, which is very beneficial for learning discriminative features from the foreground areas at the deeper layers. Extensive experiments demonstrate that the semantically consistent attention network can cope with the above challenges. The results display that the matching rate (Rank-1) of the proposed algorithm on datasets Market1501, DukeMTMC-reID and CUHK03 is 93.9%, 87.3%, 73.4%, and the mean Average Precision (mAP) is 90.8%, 82.2%, 75.2%, respectively.