FotoFile: a consumer multimedia organization and retrieval system
01 May 1999-pp 496-503
TL;DR: FotoFile is an experimental system for multimedia organization and retrieval, based upon the design goal of making multimedia content accessible to non-expert users that blends human and automatic annotation methods.
Abstract: FotoFile is an experimental system for multimedia organization and retrieval, based upon the design goal of making multimedia content accessible to non-expert users. Search and retrieval are done in terms that are natural to the task. The system blends human and automatic annotation methods. It extends textual search, browsing, and retrieval technologies to support multimedia data types.
Citations
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TL;DR: A comprehensive and critical survey of face detection algorithms, ranging from simple edge-based algorithms to composite high-level approaches utilizing advanced pattern recognition methods, is presented.
1,565 citations
29 Apr 2007
TL;DR: The incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr are investigated to offer a taxonomy of motivations for annotation along two dimensions (sociality and function).
Abstract: Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture. A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.
912 citations
Cites background from "FotoFile: a consumer multimedia org..."
...Work in [3, 5, 8, 21, 25] addressed ease and partial automation of the labeling task on one hand, and expanding the benefits of annotation on the other....
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...Providing tools for annotation of media is therefore an active field of research in human-computer interaction [3, 8, 21]....
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01 May 2000
TL;DR: Based on a workshop discussion of multiple views, and based on the authors' own design and implementation experience with these systems, eight guidelines for the design of multiple view systems are presented.
Abstract: A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems.
794 citations
Cites methods from "FotoFile: a consumer multimedia org..."
...As an example, one of the authors of this paper worked on a system, FotoFile, which supports the organization of digital photos and video [17]....
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TL;DR: Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.
Abstract: A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.
585 citations
Additional excerpts
...A reasonable treatment for multiview face detection and recognition in the appearance-based framework is the view-based method [29], whereby difficulties in explicit 3D modeling are avoided....
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Patent•
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TL;DR: The file system shell as discussed by the authors provides virtual folders which expose regular files and folders to users in different views based on their metadata instead of the actual physical underlying file system structure on the disk.
Abstract: A file system shell is provided. One aspect of the shell provides virtual folders which expose regular files and folders to users in different views based on their metadata instead of the actual physical underlying file system structure on the disk. Users are able to work with the virtual folders through direct manipulation (e.g., clicking and dragging, copying, pasting, etc.). Filters are provided for narrowing down sets of items. Quick links are provided which can be clicked on to generate useful views of the sets of items. Libraries are provided which consist of large groups of usable types of items that can be associated together, along with functions and tools related to the items. A virtual address bar is provided which comprises a plurality of segments, each segment corresponding to a filter for selecting content. A shell browser is provided with which users can readily identify an item based on the metadata associated with that item. An object previewer in a shell browser is provided which is configured to display a plurality of items representing multiple item types.
544 citations
References
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14,948 citations
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
14,562 citations
Book•
01 Jan 1973
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
13,647 citations
Book•
01 Jan 1983
TL;DR: In this paper, the authors present an EPISODIC/SEMANTIC DISTINCTION and a general overview of the ECPHORY system in a general framework.
Abstract: PART I: EPISODIC/SEMANTIC DISTINCTION PART II: GENERAL ABSTRACT PROCESSING SYSTEM PART III: SYNERGISTIC ECPHORY
4,757 citations
TL;DR: A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Abstract: We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.
4,105 citations