Automated Labeling Process for Unknown Images in an open-world Scenario

Abstract

Most of the recognition systems presume a controlled, well-defined research setting, where all possible classes that can appear during a test are known a priori. This environment is referred to as the ``closed-world'' model, while the ``open-world'' model implies that unknown classes can be incorporated into a recognition algorithm whilst being predicted. Therefore, recognition systems that operate in the real world have to deal with these unknown categories. Our objective was not only to detect data that originate from categories unseen during training, but to identify similarities between pieces of unknown data and then form new classes by automatically labeling them. Our Double Probability Model was extended by an image clustering algorithm, in which Kernel K-means was used. A new procedure, namely the Cluster Classification algorithm for the detection of unknowns and automated labeling, is proposed. These approaches facilitate the transition from open-set recognition to an open-world problem. The Fisher Vector (FV) was used for the mathematical representation of the images and then a Support Vector Machine introduced as a classifier. The measurement of similarity was based on the FV representations. Experiments were conducted on the Caltech101 and Caltech256 datasets of images and the Rand Index was evaluated over the unknown data. The results showed that our proposed Cluster Classification algorithm was able to yield almost the same Rand Index, even though the number of unknown categories increased.