Fine‐grained classification tasks are challenging because fine‐grained data sets are quite scarce. Thus, we utilized the domain adaptation method to migrate knowledge from large, labeled data sets to fine‐grained target data sets. We employed the bin similarity (BS) algorithm to measure and select the approximate domains from large‐scale data sets to the fine‐grained target domains. Source domain feature space was divided into multiple bins and the features of the target domains were sampled to fill the bins. The most similar domains were selected based on the similarity statistics of the sample features. We implemented the BS algorithm combined with the popular convolutional neural networks, pretrained the network on the selected similar subdata sets, and subsequently fine‐tuned it on the fine‐grained data sets. We evaluated the BS classification model on Stanford Dogs and Oxford Flower data sets, and the results showed improved BS classification performance compared with the state‐of‐the‐art domain adaptation methods, earth mover's distance, selective joint fine‐tuning, L2 with starting point, and domain similarity for transfer learning. Furthermore, BS is a pluggable module that boosts the performance of domain adaptation.

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Through semantically grouping pixels in local neighborhoods, superpixels can capture image redundancy and significantly improve the performance of post-processing algorithms. In this paper, we investigate the application of superpixels in FCM framework, and propose a modified FCM algorithm SPFCM which utilizes superpixels as clustering objects instead of pixels. Superpixel and its neighborhood increase the clustering granularity and allow us to compute the objective function on a naturally adaptive domain rather than on a fixed window, so our algorithm can make full use of the spatial information and is more robust to noise. Due to the compact image representation based on superpixels, the computational complexity of our method is also drastically reduced. Experimental results on both synthetic and real images demonstrate the effectiveness and efficiency of our algorithm.