Active Constraint Learning (ACL) is continuously gaining popularity in the area of constrained clustering due to its ability to achieve performance gains via incorporating minimal feedback from a human annotator for selected instances. For constrained clustering algorithms, such instances are integrated in the form of Must-Link (ML) and Cannot-Link (CL) constraints. Existing iterative uncertainty reduction schemes, introduce high computational burden particularly when they process larger datasets that are usually present in computer vision and visual learning applications. For scenarios that multiple agents (i.e., robots) require user feedback for performing recognition tasks, minimizing the interaction between the user and the agents, without compromising performance, is an essential task. In this study, a non-iterative ACL scheme with proven performance benefits is presented. We select to demonstrate the effectiveness of our methodology by building on the well known K-Means algorithm for clustering; one can easily extend it to alternative clustering schemes. The proposed methodology introduces the use of the Silhouette values, conventionally used for measuring clustering performance, in order to rank the degree of information content of the various samples. In addition, an efficient greedy selection scheme was devised for selecting the most informative samples for human annotation. To the best of our knowledge, this is the first active constrained clustering methodology with the ability to process computer vision datasets that this study targets. Performance results are shown on various computer vision benchmarks and support the merits of adopting the proposed scheme.
|Original language||English (US)|
|Title of host publication||IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|State||Published - Nov 28 2016|
|Event||2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 - Daejeon, Korea, Republic of|
Duration: Oct 9 2016 → Oct 14 2016
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Other||2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016|
|Country||Korea, Republic of|
|Period||10/9/16 → 10/14/16|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation through grants #IIP-0934327, #CNS-1039741, #SMA-1028076, #CNS-1338042, #IIS-1427014, #IIP-1439728, #IIP-1432957, #OIA-1551059 and #CNS-1514626.
© 2016 IEEE.
Copyright 2017 Elsevier B.V., All rights reserved.
- Active constrained clustering
- Image clustering uncertainty management
- Visual learning