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 Proposed method – Selection of best algorithm for given image • Decision of several trees on heterogeneous features – Representing features » Image content in terms of low level visual properties – Trained trees for selection » Minimization of expected error in illuminant estimation • Estimating illuminant as weighted sum of different algorithms(More)
The paper describes a new indexing methodology for image databases integrating color and spatial information for content-based image retrieval. This methodology, called Spatial-Chromatic Histogram (SCH), synthesizing in few values information about the location of pixels having the same color and their arrangement within the image, can be more satisfactory(More)
Color is a feature of the great majority of content-based image retrieval systems. However the robustness, effectiveness, and efficiency of its use in image indexing are still open issues. This paper provides a comprehensive survey of the methods for color image indexing and retrieval described in the literature. In particular, image preprocessing, the(More)
In this work, we investigate how illuminant estimation techniques can be improved, taking into account automatically extracted information about the content of the images. We considered indoor/outdoor classification because the images of these classes present different content and are usually taken under different illumination conditions. We have designed(More)
The paper addresses the problem of distinguishing between pornographic and non-pornographic photographs, for the design of semantic filters for the web. Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM), have been used to perform the classification. The photographs(More)
Video summarization, aimed at reducing the amount of data that must be examined in order to retrieve the information desired from information in a video, is an essential task in video analysis and indexing applications. We propose an innovative approach to the selection of representative (key) frames of a video sequence for video summarization. By analyzing(More)
We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1027 canteen trays for a total of 3616 food instances belonging to(More)
— We propose a new self-adaptive image cropping algorithm where the processing steps are driven by the classification of the images into semantic classes. The algorithm exploits both visual and semantic information. Visual information is obtained by a visual attention model, while semantic information relates to the automatically assigned image genre and to(More)