'CBIR'에 해당되는 글 4건

  1. 2009.01.21 Similarity Based Image Retrieval System (4)
  2. 2007.06.06 Barcamp Seoul (6)
  3. 2006.08.14 CBIR (1)
  4. 2006.08.14 Content-based image retrieval (1)
search2009.01.21 12:00
from http://gizmodo.com/340788/hitachi-builds-15+inch-ultra-thin-plasma-to-go-with-its-15+inch-lcds

Similarity Based Image Retrieval System - With the volume of data already at unprecedented levels and expected to continue to increase rampantly, technology enabling quick searches of still and video images is much in demand. In response, Hitachi has developed a Similarity-Based Image Retrieval technology, a search engine for just such large-scale image and video archives. Similarity-Based Image Retrieval technology automatically extracts quantified information intrinsic to the image — such as color, shapes and forms — and runs searches to locate a match. This innovative search technique can be used for something as basic as searching for a movie scene or image on a camcorder to something as complex as searching for facial imagery in security, video surveillance or law enforcement applications.

Hitachi 연구소에서 진해되어왔던 프로젝트가 적용되어 빛을 보는 것 같습니다. 하지만 데모 이미지가 없어서 성능이 어느 정도인지는 알 수가 없습니다. 이전에 CHI나 UIST에 만났던 연구원들은 꽤 재미있는 일들을 많이 하는 것 같아서 부러웟던 기억이 나네요. 
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Posted by myditto
search2007.06.06 23:38
사용자 삽입 이미지

barcamp Seoul


barcamp
를 처음으로 참가하다... 워낙 내 자신을 드러내지 않는 성격에 이런 오프라인 모임에 나가기가 쉽지 않았습니다. 하지만 이제는 내가 하고 있는 일들이 사용자들에게 얼마나 유익한 일들인지 알리고자 한다. 처음 만나는 사람을 본다는 생각에 설레이기도 하며
겁이 나기도 했다. 내가 과연 생각했던대로 내 생각을 잘 전달할 수 있을까? 개인적으로는 나에게는 가르침의 달란트가 없다는 걸 잘 알기 때문이다. 진정으로 절대 고수들은 어려운 내용도 아주 쉽게 설명할 수 있다. 예를 들면 파인만 아저씨의 물리책 말이다.. 아직 난 고수는 아닌 것 같다. 내공을 쌓아야겠다... 아래는 발표한 자료 content based image retrieval 이다.
처음 만나는 분들이지만, 웹 이라는 울타리 안에서 다양한 얘기를 들으니 책임감도 느끼고 배운 내용도 참 많았다. 내 발표 내용은 듣는 분들에게 조금 어려운 내용이라는 생각과 함께 다음에는 좀 더 쉬운 이미지 검색 얘기를 해야겠다. 그러고 보니 블로그를 연지 처음으로 한국말로 썼네...
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Posted by myditto
search2006.08.14 14:08

External links

  • CIRES developed by the University of Texas at Austin.
  • Tiltomo : Image Visual Search EngineCBIR (content based image retrieval) system uses advanced proprietarySubject, Color & Texture recognition algorithms to analyze imagecomposition.
  • our.imgSeek - Site for social photo bookmarking: search images by similarity, sketch, tag, rate and get recommendations.
  • IKONA - Online demonstration - Generic CBIR system - INRIA - IMEDIA
  • SIMPLIcity and ALIP online Demos developed by Stanford and Penn State Universities
  • GIFT - The GNU Image Finding Tool - an open source query by example CBIRS
    • Viper Demo - an online demonstration of the GIFT
    • Perl MRML Client - another GIFT demo, using a different client, and combining textual annotation with visual features
  • SIMBA- demo of the Search Images By Appearance system by theAlbert-Ludwigs-Universität Freiburg (Germany) - Inst. for PatternRecognition and Image Processing
  • FIRE online demo, FIRE homepage FIRE (Flexible Image Retrieval Engine) is another open source query by example CBIRS
  • LCPD: Leiden 19th-Century Portrait Database - an online database of 19th century studio portraits searchable via CBIR and commonly referenced in the literature
  • imgSeek - opensource photo collection manager and viewer with content-based search and many other features
  • Video Google demo - search movies for specific objects
  • Cortina - Content Based Image Retrieval for 3 Million Images. From UCSB.
  • eVision - Go Beyond Keywords! Perform a Visual Image Search.
  • Octagon - Free Java based Content-Based Image Retrieval software.
  • Retrievr - search and explore in a selection of Flickr images by drawing a rough sketch or uploading an image.
  • LTU technologies- LTU tech has deployed CBIR and automatic image classificationapplications in the media market, the IP protection market and the lawenforcement / computer forensics market. Online demo on Corbis images.
  • PicSOM CBIR tool, developed in the Laboratory of Computer and Information Science, Helsinki University of Technology.
  • LIRE - Lucene Image Retrieval Java CBIR library, which uses the Lucene search engine
  • MUVIS - MUVIS Image and Video Retrieval CBIR System at TUT- Tampere University of Technology.
  • xcavator - an interactive image search demo integrated with Flickr. Powered by technology developed by CogniSign.
  • IN2 intelligent indexing - provides multimedia content management solutions including content-based image and video retrieval.
[edit]

Relevant research papers

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Posted by myditto
search2006.08.14 11:37

Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases."Content-based" means that the search makes use of the contents of theimages themselves, rather than relying on human-inputted metadata such as captions or keywords. A content-based image retrieval system (CBIRS) is a piece of software that implements CBIR.

The term CBIR seems to have originated in 1992, when it was used byT. Kato to describe experiments into automatic retrieval of images froma database, based on the colours and shapes present. Since then, theterm has been used to describe the process of retrieving desired imagesfrom a large collection on the basis of syntactical image features. Thetechniques, tools and algorithms that are used originate from fieldssuch as statistics, pattern recognition, signal processing, andcomputer vision.

There is growing interest in CBIR because of the limitationsinherent in metadata-based systems. Textual information about imagescan be easily searched using existing technology, but requires humansto personally describe every image in the database.This is impracticalfor very large databases, or for images that are generatedautomatically, e.g. from surveillance cameras.It is also possible to miss images that use different synonyms in theirdescriptions. Systems based on categorizing images in semantic classeslike "cat" as a subclass of "animal" avoid this problem but still facethe same scaling issues.

The ideal CBIR system from a user perspective would involve what is referred to as semanticretrieval, where the user makes a request like "find pictures of dogs"or even "find pictures of Abraham Lincoln". This type of open-endedtask is very difficult for computers to perform - pictures ofchihuahuas and Great Danes look very different, and Lincoln may notalways be facing the camera or in the same pose. Current CBIR systemstherefore generally make use of lower-level features like texture,color, and shape, although some systems take advantage of very commonhigher-level features like faces (see facial recognition system).Not every CBIR system is generic. Some systems are designed for aspecific domain, e.g. shape matching can be used for finding partsinside a CAD-CAM database.

Different implementations of CBIR make use of different types of user queries.

  • With query by example, the user searches with a query image(supplied by the user or chosen from a random set), and the softwarefinds images similar to it based on various low-level criteria.
  • With query by sketch, the user draws a rough approximationof the image they are looking for, for example with blobs of color, andthe software locates images whose layout matches the sketch.
  • Other methods include specifying the proportions of colors desired(e.g. "80% red, 20% blue") and searching for images that contain anobject given in a query image (as at [1]).

CBIR systems can also make use of relevance feedback, wherethe user progressively refines the search results by marking images inthe results as "relevant", "not relevant", or "neutral" to the searchquery, then repeating the search with the new information.

One application of CBIR is to identify images with skin-tones and shapes that could indicate the presence of nudity, for filtering and for searching by law enforcement.

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from wikipedia

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