<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<identifier>8JMKD3MGPBW34M/3JMP3KB</identifier>
		<repository>sid.inpe.br/sibgrapi/2015/06.19.21.42</repository>
		<lastupdate>2015:06.19.21.42.54 sid.inpe.br/banon/2001/03.30.15.38 letriciapsa@gmail.com</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi/2015/06.19.21.42.54</metadatarepository>
		<metadatalastupdate>2020:02.19.02.14.04 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2015}</metadatalastupdate>
		<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>
		<size>1705 KiB</size>
		<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</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<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>
		<targetfile>PID3758331_cameraReady.pdf</targetfile>
		<usergroup>letriciapsa@gmail.com</usergroup>
		<visibility>shown</visibility>
		<documentstage>not transferred</documentstage>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<nexthigherunit>8JMKD3MGPBW34M/3K24PF8</nexthigherunit>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2015/06.19.21.42</url>
	</metadata>
</metadatalist>