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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. Thedoi:10.1145/2671188.2749314 dblp:conf/mir/SongMD15 fatcat:s3nzs7srbfgbblizxge3xeuz5e