  We now know that mid-level features can greatly enhance the performance of
image learning, but how to automatically learn the image features efficiently
and in an unsupervised manner is still an open question. In this paper, we
present a very efficient mid-level feature learning approach (MidFea), which
only involves simple operations such as $k$-means clustering, convolution,
pooling, vector quantization and random projection. We explain why this simple
method generates the desired features, and argue that there is no need to spend
much time in learning low-level feature extractors. Furthermore, to boost the
performance, we propose to model the neuron selectivity (NS) principle by
building an additional layer over the mid-level features before feeding the
features into the classifier. We show that the NS-layer learns
category-specific neurons with both bottom-up inference and top-down analysis,
and thus supports fast inference for a query image. We run extensive
experiments on several public databases to demonstrate that our approach can
achieve state-of-the-art performances for face recognition, gender
classification, age estimation and object categorization. In particular, we
demonstrate that our approach is more than an order of magnitude faster than
some recently proposed sparse coding based methods.
