Tasting Families of Features for Image Classification
Please find below the code necessary to reproduce the experiments of the paper "Tasting Families of Features for Image Classification" under the GPL v2 license.
Using multiple families of image features is a very efficient strategy to improve performance in object detection or recognition. However, such a strategy induces multiple challenges for machine learning methods, both from a computational and a statistical perspective.
The main contribution of this paper is a novel feature sampling procedure dubbed “Tasting” to improve the efficiency of Boosting in such a context. Instead of sampling features in a uniform manner, Tasting continuously estimates the expected loss reduction for each family from a limited set of features sampled prior to the learning, and biases the sampling accordingly.
We evaluate the performance of this procedure with tens of families of features on four image classification and object detection data-sets. We show that Tasting, which does not require the tuning of any meta-parameter, outperforms systematically variants of uniform sampling and state-oft he- art approaches based on bandit strategies.
You can download the archive here: tasting.tar.bz2
And the reference paper here : Tasting Families of Features for Image Classification