21,345 research outputs found

    Labeling Faces Victimization Bunch Primarily Based Internet Pictures Annotation to Produce Authentication in Security

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    Auto face annotation is important in abounding absolute apple advice administration systems. Face tagging in images and videos enjoys abounding abeyant applications in multimedia advice retrieval. Face comment is a meadow of face apprehension and recognition. Mining abominably labeled facial images on the internet shows abeyant classic appear auto face annotation. This blazon of classic motivates the new assay botheration of defended authentication. The ambition of the arrangement is to comment disregarded faces in images and videos with the words that best alarm the image. A framework called seek based face comment (SBFA) provides the way to abundance abominably labeled facial images. Facial images that are accessible on Apple Wide Web (WWW) or the angel database created by the aegis administration can be annotated. A one arduous botheration with the seek based face comment arrangement is how finer accomplish comment by advertisement agnate facial images and their anemic labels which are blatant and incomplete. To affected this botheration proposed admission uses unsupervised characterization clarification (ULR) to clarify the labels of web facial images. To acceleration up the proposed arrangement a absorption based approximation algorithm is used. Uses of comment will advice for user to seek admiration angel and video. As well if arrangement gets implemented in amusing arrangement again it will affected the check of accepted absolute arrangement which tags manually

    Interesting faces: A graph-based approach for finding people in news

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    Cataloged from PDF version of article.In this study, we propose a method for finding people in large news photograph and video collections. Our method exploits the multi-modal nature of these data sets to recognize people and does not require any supervisory input. It first uses the name of the person to populate an initial set of candidate faces. From this set, which is likely to include the faces of other people, it selects the group of most similar faces corresponding to the queried person in a variety of conditions. Our main contribution is to transform the problem of recognizing the faces of the queried person in a set of candidate faces to the problem of finding the highly connected sub-graph (the densest component) in a graph representing the similarities of faces. We also propose a novel technique for finding the similarities of faces by matching interest points extracted from the faces. The proposed method further allows the classification of new faces without needing to re-build the graph. The experiments are performed on two data sets: thousands of news photographs from Yahoo! news and over 200 news videos from TRECVid2004. The results show that the proposed method provides significant improvements over textbased methods. (C) 2009 Elsevier Ltd. All rights reserve

    RGB-D datasets using microsoft kinect or similar sensors: a survey

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    RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms
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