ULDF is proposed to make better use of autoencoder for image recognition. It is performed on local patches rather than whole images, which helps to scale the algorithm to realistic-sized images.Owning to the combination with BoW, it is more robust to local translation.The short codes transformed from vectorized patches using autoencoder have good low-dimensional property. This property makes it consume less time and less memory while clustering. Moreover, short codes make it possible to use finer level spatial pyramid, such as 2 × 2 and 4 × 4.It only requires an efficient linear SVM classifier and obtains state-of-the-art classify performance on MNIST. Unsupervised feature learning is an important problem in computer vision and machine learning. Various unsupervised learning methods, e.g., PCA, KMeans, autoencoder, have been applied for visual recognition tasks. Especially, autoencoder has superior image recognition performance, which is usually used as a global feature extractor. To make better use of autoencoder for image recognition, we propose unsupervised local deep feature (ULDF). We collect patches from training images and use autoencoder to transform vectorized patches into short codes. Then these codes are encoded using Fisher Vector with spatial pyramid. Image recognition with ULDF only requires an efficient linear SVM classifier. Finally, we perform multi-scale voting to achieve more accurate classification performance. In the experiments, we evaluate ULDF on the handwritten digit recognition task and the shape classification task, and show that it achieves highly competitive performance comparing to the state-of-the-art image recognition methods. The results suggest that the proposed local deep feature is superior than traditional global autoencoder.
['Yang Wang', 'Xinggang Wang', 'Wenyu Liu']
Unsupervised local deep feature for image recognition