Building holistic descriptors for scene recognition: a multi-objective genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/mm/LiuSL13,

• author = "Li Liu and Ling Shao and Xuelong Li",
• title = "Building holistic descriptors for scene recognition: a multi-objective genetic programming approach",
• booktitle = "Proceedings of the 21st ACM international conference on Multimedia",
• year = "2013",
• editor = "Alejandro Jaimes and Nicu Sebe and Nozha Boujemaa and Daniel Gatica-Perez and David A. Shamma and Marcel Worring and Roger Zimmermann",
• publisher = "ACM",
• pages = "997--1006",
• address = "Barcelona, Spain",
• month = oct # " 21-25",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2013-11-14",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mm/mm2013.html#LiuSL13",
• isbn13 = "978-1-4503-2404-5",
• URL = "http://dl.acm.org/citation.cfm?id=2502081",
• URL = "http://doi.acm.org/10.1145/2502081.2502095",
• DOI = "doi:10.1145/2502081.2502095",
• acmid = "2502095",
• size = "10 pages",
• abstract = "Real-world scene recognition has been one of the most challenging research topics in computer vision, due to the tremendous intra-class variability and the wide range of scene categories. In this paper, we successfully apply an evolutionary methodology to automatically synthesise domain-adaptive holistic descriptors for the task of scene recognition, instead of using hand-tuned descriptors. We address this as an optimisation problem by using multi-objective genetic programming (MOGP). Specifically, a set of primitive operators and filters are first randomly assembled in the MOGP framework as tree-based combinations, which are then evaluated by two objective fitness criteria i.e., the classification error and the tree complexity. Finally, the best-so-far solution selected by MOGP is regarded as the (near-)optimal feature descriptor for scene recognition. We have evaluated our approach on three realistic scene datasets: MIT urban and nature, SUN and UIUC Sport. Experimental results consistently show that our MOGP-generated descriptors achieve significantly higher recognition accuracies compared with state-of-the-art hand-crafted and machine-learnt features.",

}

Genetic Programming entries for Li Liu Ling Shao Xuelong Li

Citations