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Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate information systems to efficiently manage these collections. The commonest approaches use the so-called Content-Based Image Retrieval (CBIR) systems. Basically, these systems try to retrieve(More)
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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)
The effectiveness of CBIR systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However , feature combination without using similarity functions does not always make sense and the combined similarity function may have to be more complex than weight-based functions in order to(More)
This paper proposes a new rotation-invariant and scale-invariant representation for texture image retrieval based on steerable pyramid decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering(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)
This work exploits the resemblance between content-based image retrieval and image analysis with respect to the design of image descriptors and their effectiveness. In this context, two shape descriptors are proposed: contour saliences and segment saliences. Contour saliences revisits its original definition, where the location of concave points was a(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)