Deep Bottleneck Feature for Image Classification

Yan Song, Ian McLoughLin, Lirong Dai
2015 Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15  
Effective image representation plays an important role for image classification and retrieval. Bag-of-Features (BoF) is well known as an effective and robust visual representation. However, on large datasets, convolutional neural networks (CNN) tend to perform much better, aided by the availability of large amounts of training data. In this paper, we propose a bag of Deep Bottleneck Features (DBF) for image classification, effectively combining the strengths of a CNN within a BoF framework. The
more » ... DBF features, obtained from a previously well-trained CNN, form a compact and low-dimensional representation of the original inputs, effective for even small datasets. We will demonstrate that the resulting BoDBF method has a very powerful and discriminative capability that is generalisable to other image classification tasks.
doi:10.1145/2671188.2749314 dblp:conf/mir/SongMD15 fatcat:s3nzs7srbfgbblizxge3xeuz5e