Video Shot Characterization Using Principles of Perceptual Prominence and Perceptual Grouping in Spatio–Temporal Domain
01 Dec 2007-IEEE Transactions on Circuits and Systems for Video Technology (IEEE)-Vol. 17, Iss: 12, pp 1728-1741
TL;DR: A computational model for analyzing a video shot based on a novel principle of perceptual prominence that captures the key aspects of mise-en-scene required for characterizing a video scene.
Abstract: We present a novel approach for applying perceptual grouping principles to the spatio-temporal domain of video. Our perceptual grouping scheme, applied on blobs, makes use of a specified spatio-temporal coherence model. The grouping scheme identifies the blob cliques or perceptual clusters in the scene. We propose a computational model for analyzing a video shot based on a novel principle of perceptual prominence. The principle of perceptual prominence captures the key aspects of mise-en-scene required for characterizing a video scene.
Citations
More filters
TL;DR: A novel approach is proposed for perceptual grouping and localization of ill-defined curvilinear structures by gradually shifting from an exploratory to an exploitative mode and compared to prior methods on synthetic and annotated real data, showing high precision rates.
Abstract: In this paper, a novel approach is proposed for perceptual grouping and localization of ill-defined curvilinear structures. Our approach builds upon the tensor voting and the iterative voting frameworks. Its efficacy lies on iterative refinements of curvilinear structures by gradually shifting from an exploratory to an exploitative mode. Such a mode shifting is achieved by reducing the aperture of the tensor voting fields, which is shown to improve curve grouping and inference by enhancing the concentration of the votes over promising, salient structures. The proposed technique is validated on delineating adherens junctions that are imaged through fluorescence microscopy. However, the method is also applicable for screening other organisms based on characteristics of their cell wall structures. Adherens junctions maintain tissue structural integrity and cell-cell interactions. Visually, they exhibit fibrous patterns that may be diffused, heterogeneous in fluorescence intensity, or punctate and frequently perceptual. Besides the application to real data, the proposed method is compared to prior methods on synthetic and annotated real data, showing high precision rates.
29 citations
Cites background from "Video Shot Characterization Using P..."
...Fromwhen it was initially conceived by the Gestalt psychologists [2] to now, perceptual grouping has evolved from the passive observation of human behavior to its inclusion in a wide-range of computer vision applications [3]–[6]....
[...]
References
More filters
01 Dec 2001
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
18,620 citations
"Video Shot Characterization Using P..." refers methods in this paper
...For scenes with stationary humans as foreground subjects, a face detector [33] is used to identify the frontal face regions in the scene....
[...]
Book•
01 Jan 1988TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
15,671 citations
13 Oct 2003
TL;DR: An approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video, represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion.
Abstract: We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching in two full length feature films.
6,938 citations
"Video Shot Characterization Using P..." refers background in this paper
...Object centric descriptions facilitate an object oriented search [29], [30]....
[...]
Book•
01 Jan 1935
TL;DR: Routledge is now reissuing this prestigious series of 204 volumes originally published between 1910 and 1965, including works by key figures such as C.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs as discussed by the authors.
Abstract: Routledge is now re-issuing this prestigious series of 204 volumes originally published between 1910 and 1965. The titles include works by key figures such asC.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs. Each volume is available on its own, as part of a themed mini-set, or as part of a specially-priced 204-volume set. A brochure listing each title in the "International Library of Psychology" series is available upon request.
4,169 citations
Book•
01 Jan 1979
TL;DR: In this paper, Bordwell and Thompson's Film Art has been the best-selling and most widely respected introduction to the analysis of cinema, supporting a skills-centered approach supported by examples from many periods and countries.
Abstract: Film is an art form with a language and an aesthetic all its own. Since 1979, David Bordwell and Kristin Thompson's Film Art has been the best-selling and most widely respected introduction to the analysis of cinema. Taking a skills-centered approach supported by examples from many periods and countries, the authors help students develop a core set of analytical skills that will enrich their understanding of any film, in any genre. In-depth examples deepen students' appreciation for how creative choices by filmmakers affect what viewers experience and how they respond.
1,561 citations
Additional excerpts
...A film-maker or a painter uses the arrangement of the mise-en-scène [25] to direct our attention across the visualization space (a scene in space and time)....
[...]