DROP: A Data Reduction and Organization Paradigm and its Application in Image Analysis

Abstract

In this paper, we deal with the problem of the annotation process in image analysis. This problem refers to the trade-off, wherein the human knowledge is indispensable for the success of the process and human's time and effort are precious resources. Can the human annotate a minimum number of images and the classifier label the remaining ones with high accuracy? Active learning techniques have been investigated to answer this question. However, these techniques very often ignore the need for interactive response times during the active learning process. They usually adopt a common paradigm which is impractical considering large datasets. We propose an effective and efficient Data Reduction and Organization Paradigm for image analysis. In our paradigm, the proposed active learning methods should be able to reduce and/or organize the large dataset such that sample selection does not require to reprocess it entirely at each learning iteration. Moreover, it can be interrupted as soon as a desired number of samples from the reduced and organized dataset is identified. These methods show an increasing progress, first with data reduction only, and then with subsequent organization of the reduced dataset. Experimental results have demonstrated the robustness of the proposed paradigm using datasets from distinct applications and baseline approaches.