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.

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We present two promising Relevance Feedback methods based on Genetic Algorithms used to enhance the performance on the task of image retrieval according to the user's interests. The first method adjusts the dissimilarity function by using weighting functions while the second method redefines the feature space by means of linear and nonlinear transformation functions. Experimental results on real datasets demonstrate that our methods are effective and the results show that the transformation approach outperforms the weighting approach, achieving a precision gain of up to 70%.

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Substantial benefits can be gained from effective Relevance Feedback techniques in content-based image retrieval. However, existing techniques are limited due to computational cost and/or by being restricted to linear transformations on the data. In this study we analyze the role of nonlinear transformations in relevance feedback. We present two promising Relevance Feedback methods based on Genetic Algorithms used to enhance the performance on the task of image retrieval according to the user's interests. The first method adjusts the dissimilarity function by using weighting functions while the second method redefines the features space by means of linear and nonlinear transformation functions. Experimental results on real data sets demonstrate that our methods are effective and the results show that the transformation approach outperforms the weighting approach, achieving a precision gain of up to 70%. Our results indicate that nonlinear transformations have a great potential in capturing the user's interests in image retrieval and should be further analyzed employing other learning/optimization mechanisms 1 .

Nowadays, Geographical Information Systems (GIS) have expanded their functionalities including larger interactive displays exploration of spatiotemporal data with several views. These systems maintain a traditional navigation method based on keyboard and mouse, interaction devices not well suited for large screens nor for collaborative work. This paper aims at showing the applicability of new devices to fill the usability gap for the scenario of large screens presentation, interaction and collaboration. New gesture-based devices have been proposed and adopted in games and medical applications, for example. This paper presents the NInA Framework, which allows an integration of natural user interface (NUI) on GIS, with the advantage of being expandable, as new demands are posed to that systems. The validation process of our NInA Kinect-based framework was made through user experiments involving specialists and non-specialists in TerrainViewer, a geographical information system, as well as experts and non-experts in the Kinect technology. The results showed that a NUI approach demands a short learning time, with just a couple of interactions and instructions, and the user is ready to go. Moreover, the users demonstrated greater satisfaction, leading to their productivity improvement.

Background: There are substantial benefits to be gained from ranking optimization in several information retrieval and recommendation systems. However, the analysis of ranking evaluation functions (REFs), which play a major role in many ranking optimization models, needs to be further investigated. An analysis of previous studies that investigated REFs was performed, and evidence was found which indicated that the choice of a proper REF is context sensitive. Methods:In this study, we analyze a broad set of REFs for feature weighting aimed at increasing the image retrieval effectiveness. The REFs analyzed sums ten and includes the most successful and representative REFs from the literature. The REFs were embedded into a genetic algorithm (GA)-based relevance feedback (RF) model, called WLSP-C±, aimed at improving image retrieval results through the use of learning weights for image descriptors and image regions. Results: Analyses of precision-recall curves in five real-world image data sets showed that one non-parameterized REF named F5, not analyzed in previous studies, overcame recommended ones, which require parameter adjustment. We also provided a computational analysis of the GA-based RF model investigated, and it was shown that it is linear in regard to the image data set cardinality. Conclusions:We conclude that REF F5 should be investigated in other contexts and problem scenarios centered on ranking optimization, as ranking optimization techniques rely heavily on the ranking quality measure.