4,498 research outputs found
Indexing and retrieval scheme of the image database based on color and spatial relations
[[abstract]]We propose a new approach to retrieve images from an image database. We combine both color and spatial features of a picture to index and measure the similarity of images. We propose a new automatic indexing scheme of the image database according to our clustering method which could filter the image efficiently. As a technical contribution, a Seed-Filling like algorithm that could extract the shape and spatial relationship feature of the image is proposed. Also, the system is incorporated with a visual interface, which allows the user to specify objects as the spatial specification of pictures. With color identification and spatial similarity functions, the preliminary experience shows that the system is able to retrieve image information of a very high satisfaction.[[conferencetype]]國際[[conferencedate]]20000730~20000802[[booktype]]紙本[[conferencelocation]]New York, NY, US
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events
As amounts of publicly available video data grow the need to query this data efficiently becomes significant. Consequently content-based retrieval of video data turns out to be a challenging and important problem. We address the specific aspect of inferring semantics automatically from raw video data. In particular, we introduce a new video data model that supports the integrated use of two different approaches for mapping low-level features to high-level concepts. Firstly, the model is extended with a rule-based approach that supports spatio-temporal formalization of high-level concepts, and then with a stochastic approach. Furthermore, results on real tennis video data are presented, demonstrating the validity of both approaches, as well us advantages of their integrated us
[[alternative]]A Flexible Content-based Image Retrieval System Integrating with Color, Shape and Spatial Relations
計畫編號:NSC89-2218-E032-013研究期間:200008~200107研究經費:856,000[[sponsorship]]行政院國家科學委員
Al Hybrid Content-Based Retrieval Approach For Video Data
Increasing use of multimedia data makes it crucial to develop intelligent search mec:hanisms for retrieving multimedia data by content. Traditional text-based methods clearly do not suffice to describe the rich content of images, voice or video. Digital vidseo requires the incorporation of temporal information for any effective contentbased retrieval scheme. We present a novel technique which integrates object motion ancl temporal relationship information in order to characterize the events for subsequent search for similar clips. We propose a hybrid mechanism based on object motion trails similarity match and interval-based temporal modeling that leads to a unique framework for spatio-temporal content based access in digital video. We implemented the proposed methods and demonstrated that high-level query formulation can be achieved for the aforementioned purpose. Development of such technology will enable true multimedia search engines that will accomplish what current Internet search engines like Infoseek or Excite do today for textual data
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