author = "Chino, Daniel Yashinobu Takada and Avalhais, Letricia Pereira 
                         Soares and Rodrigues Junior, Jose Fernando and Traina, Agma Juci 
          affiliation = "{University of Sao Paulo} and {University of Sao Paulo} and 
                         {University of Sao Paulo} and {University of Sao Paulo}",
                title = "BoWFire: detection of fire in still images by integrating pixel 
                         color and texture analysis",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "fire detection, still images, pixel-color classification, texture 
             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 
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3758331_cameraReady.pdf",
        urlaccessdate = "2021, Dec. 03"