Otávio Augusto Bizetto Penatti

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In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote(More)
We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train(More)
Classifying Remote Sensing Images (RSI) is a hard task. There are automatic approaches whose results normally need to be revised. The identification and polygon extraction tasks usually rely on applying classification strategies that exploit visual aspects related to spectral and texture patterns identified in RSI regions. There are a lot of image(More)
Otávio A. B. Penattia,∗, Fernanda B. Silva, Eduardo Valle, Valerie Gouet-Brunet, Ricardo da S. Torres RECOD Lab, Institute of Computing (IC), University of Campinas (Unicamp) – Av. Albert Einstein, 1251, Campinas, SP, 13083-852, Brazil Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC),(More)
In this paper, we present an unsupervised distance learning approach for improving the effectiveness of image retrieval tasks. We propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k-reciprocal neighborhood. The similarity is propagated among neighbors considering the geometry of the dataset(More)
This paper presents Eva, a tool for evaluating image descriptors for content-based image retrieval. Eva integrates the most common stages of an image retrieval process and provides functionalities to facilitate the comparison of image descriptors in the context of content-based image retrieval. Eva supports the management of image descriptors and image(More)
This work describes the approach used by the RECOD team in the MediaEval Placing Task of 2013, in which we were required to develop an automatic scheme to assign geographical locations to images. Our approach is multimodal, considering textual and visual descriptors, which are combined by a rank aggregation strategy. We estimate the location of test images(More)
This paper presents a novel approach for video representation, called bag-of-scenes. The proposed method is based on dictionaries of scenes, which provide a high-level representation for videos. Scenes are elements with much more semantic information than local features, specially for geotagging videos using visual content. Thus, each component of the(More)
This paper presents a comparative study of color descriptors for content-based image retrieval on the Web. Several image descriptors were compared theoretically and the most relevant ones were implemented and tested in two different databases. The main goal was to find out the best descriptors for Web image retrieval. Descriptors are compared according to(More)
Background: Pixel-level tissue classification for ultrasound images, commonly applied to carotid images, is usually based on defining thresholds for the isolated pixel values. Ranges of pixel values are defined for the classification of each tissue. The classification of pixels is then used to determine the carotid plaque composition and, consequently, to(More)