Balanced active learning method for image classification

The manual labeling of natural images is and has always been painstaking and slow process, especially when large data sets are involved. Nowadays, many studies focus on solving this problem, and most of them use active learning, which offers a solution for reducing the number of images that need to be labeled. Active learning procedures usually select a subset of the whole data by iteratively querying the unlabeled instances based on their predicted informativeness. One way of estimating the information content of an image is by using uncertainty sampling as a query strategy. This basic technique can significantly reduce the number of label needed; e.g. to set up a good model for classification. Our goal was to improve this method by balancing the distribution of the already labeled images. This modification is based on a novel metric that we present in this paper. We conducted experiments on two popular data sets to demonstrate the efficiency of our proposed balanced active learning (BAL) approach, and the results showed that it outperforms the basic uncertainty sampling

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks