4 research outputs found
Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework
We propose a Bayesian evidence framework to facilitate transfer learning from
pre-trained deep convolutional neural networks (CNNs). Our framework is
formulated on top of a least squares SVM (LS-SVM) classifier, which is simple
and fast in both training and testing, and achieves competitive performance in
practice. The regularization parameters in LS-SVM is estimated automatically
without grid search and cross-validation by maximizing evidence, which is a
useful measure to select the best performing CNN out of multiple candidates for
transfer learning; the evidence is optimized efficiently by employing Aitken's
delta-squared process, which accelerates convergence of fixed point update. The
proposed Bayesian evidence framework also provides a good solution to identify
the best ensemble of heterogeneous CNNs through a greedy algorithm. Our
Bayesian evidence framework for transfer learning is tested on 12 visual
recognition datasets and illustrates the state-of-the-art performance
consistently in terms of prediction accuracy and modeling efficiency.Comment: Appearing in CVPR-2016 (oral presentation
GOLD: Gaussians of Local Descriptors for Image Representation
The Bag of Words paradigm has been the baseline from which several successful image classification solutions were developed in the last decade. These represent images by quantizing local descriptors and summarizing their distribution. The quantization step introduces a dependency on the dataset, that even if in some contexts significantly boosts the performance, severely limits its generalization capabilities. Differently, in this paper, we propose to model the local features distribution with a multivariate Gaussian, without any quantization. The full rank covariance matrix, which lies on a Riemannian manifold, is projected on the tangent Euclidean space and concatenated to the mean vector. The resulting representation, a Gaussian of local descriptors (GOLD), allows to use the dot product to closely approximate a distance between distributions without the need for expensive kernel computations.
We describe an image by an improved spatial pyramid, which avoids boundary effects with soft assignment: local descriptors contribute to neighboring Gaussians, forming a weighted spatial pyramid of GOLD descriptors.
In addition, we extend the model leveraging dataset characteristics in a mixture of Gaussian formulation further improving the classification accuracy. To deal with large scale datasets and high dimensional feature spaces the Stochastic Gradient Descent solver is adopted. Experimental results on several publicly available datasets show that the proposed method obtains state-of-the-art performance
The joint submission of the tu berlin and fraunhofer first (tubfi) to the imageclef2011 photo annotation task
Abstract. In this paper we present details on the joint submission of TU Berli