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		<doi>10.1109/SIBGRAPI.2015.19</doi>
		<citationkey>ChinoAvalRodrTrai:2015:DeFiSt</citationkey>
		<title>BoWFire: detection of fire in still images by integrating pixel color and texture analysis</title>
		<format>On-line</format>
		<year>2015</year>
		<numberoffiles>1</numberoffiles>
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		<author>Chino, Daniel Yashinobu Takada,</author>
		<author>Avalhais, Letricia Pereira Soares,</author>
		<author>Rodrigues Junior, Jose Fernando,</author>
		<author>Traina, Agma Juci Machado,</author>
		<affiliation>University of Sao Paulo</affiliation>
		<affiliation>University of Sao Paulo</affiliation>
		<affiliation>University of Sao Paulo</affiliation>
		<affiliation>University of Sao Paulo</affiliation>
		<editor>Papa, Joćo Paulo,</editor>
		<editor>Sander, Pedro Vieira,</editor>
		<editor>Marroquim, Ricardo Guerra,</editor>
		<editor>Farrell, Ryan,</editor>
		<e-mailaddress>letriciapsa@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador, BA, Brazil</conferencelocation>
		<date>26-29 Aug. 2015</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
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		<versiontype>finaldraft</versiontype>
		<keywords>fire detection, still images, pixel-color classification, texture feature.</keywords>
		<abstract>Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.</abstract>
		<language>en</language>
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