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.

Relevant research papers

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.


from wikipedia

Posted by myditto
Heaven2006.08.11 10:03

이곳은 나에게 의미가 너무도 큰 곳이다. 나의 시야를 넓혀준 곳, 그리고 정말로 좋은 사람들과
항상 재미있는 생활들이 나를 설레게 하는 곳. 나의 인생에 큰 획을 그어줄 중요한 소스가 잇는 곳
Posted by myditto
media2006.08.10 09:01

wikipedia 직원이었던 분이 구글에서 강연한 내용 - 다시 보자
Posted by myditto
search2006.08.10 08:58

구글 테크노트에서 찾은 건데 어떤 내용인지 들어보장 ~~~
Posted by myditto
Heaven2006.08.10 00:33
  모여라 모여라~~~~
Posted by myditto

티스토리 툴바