@inproceedings{052a09459bb34cd4a41265a3a6047337,
title = "Active object detection on graphs via locally informative trees",
abstract = "Active object detection refers to the problem of determining the existence and location of objects in an image by actively selecting which regions of the image to explore. Herein, an object detection algorithm is proposed that models image regions as vertices and overlap relationships as edges in a directed weighted graph. Information is propagated from labeled vertices through graph edges that operate as noisy channels via message passing over locally informative trees that are extracted from the original graph using an information-theoretic criterion. Influential vertices are determined by an appropriate centrality index. Our algorithm can be applied on top of any state-of-the-art region proposal method as it treats it as a black box. The effectiveness of the proposed algorithm is illustrated on different scenarios, where in some cases only 0.45% of the total regions is evaluated.",
keywords = "active object detection, degree centrality, graphs, locally informative trees, mutual information",
author = "Zois, {Daphney Stavroula} and Maxim Raginsky",
year = "2016",
month = nov,
day = "8",
doi = "10.1109/MLSP.2016.7738876",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Kostas Diamantaras and Aurelio Uncini and Palmieri, {Francesco A. N.} and Jan Larsen",
booktitle = "2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings",
note = "26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings ; Conference date: 13-09-2016 Through 16-09-2016",
}