Loading [a11y]/accessibility-menu.js
Multi-Objective Evolutionary Metric Learning for Image Retrieval Using Convolutional Neural Network Features | IEEE Conference Publication | IEEE Xplore

Multi-Objective Evolutionary Metric Learning for Image Retrieval Using Convolutional Neural Network Features


Abstract:

For an image retrieval system, the metric to measure the similarity between target images and queries greatly affects its retrieval performance. However, most of the exis...Show More

Abstract:

For an image retrieval system, the metric to measure the similarity between target images and queries greatly affects its retrieval performance. However, most of the existing metrics are based on single distance metric, which has been shown lack of robustness for different kinds of queries. In this work, we view the metric learning task as an optimization problem for a robust combination of existing metrics, where the objective function is data-driven rather than analytical. Our contribution is two-fold. Firstly, considering the robustness of image retrieval systems, we formulate the optimization problem as a multiobjective optimization with both the average and worst retrieval performance (precision) for different kinds of queries as objectives. Secondly, we apply a popular multi-objective evolutionary algorithm, NSGA-II, to search the optimal combined metric. With the experiment on two different datasets for image retrieval, we find that the proposed algorithm can find combined metrics with better and more robust retrieval performance than other existing single metrics.
Date of Conference: 10-13 June 2019
Date Added to IEEE Xplore: 08 August 2019
ISBN Information:
Conference Location: Wellington, New Zealand

References

References is not available for this document.