imense® GazoPa similar image search article sur l'image et le droit d'auteur Par André Gunthert, samedi 7 octobre 2006 à 11:03 (5766 vues, permalink, rss co) :: Comptes rendus Plusieurs travaux récents, aux Etats-Unis ou en Allemagne, ont fait le point sur les évolutions imposées par internet et les pratiques de la photographie digitale dans le domaine du droit des images. En attendant une contribution semblable dans l'aire francophone, signalons la mise à jour de l'ouvrage de référence de Marie Cornu et Nathalie Mallet-Poujol, Droit, Oeuvres d'art et Musées. Parmi les nombreux articles traitant des problèmes liés à la photographie, on retiendra en particulier la section consacrée à "La politique française en matière d'édition d'art" (p. 471-496), qui détaille les conditions de reproduction des oeuvres. L'ouvrage rappelle l'évolution récente du droit à l'image, qui encadre désormais de façon ferme la possibilité de photographier des bâtiments et autres biens visibles, et subordonne le dépôt de plainte à l'existence d'un “trouble anormal”. Références:1.
Mappr! Where It's At. Where it’s at Photos from flickr.com (“almost certainly the best online photo management and sharing application in the world”) are often tagged with information that can be used to make educated guesses about their locations in the world. Mappr uses this data, which is provided by Flickr users and made available via the Flickr API, to place their images on a map. Images in Alaska Starting late 2004, we began collecting images from Flickr and comparing them against a U.S. Mappr was built to explore the idea of a collaborative mapped photo space, without having to wait for cameras to come with automatic GPS locators in them. Mappr has been featured in publications such as Barron’s, The Wall Street Journal, and Peter Morville’s recent book, Ambient Findability. Note: As of 2007, Mappr is no longer processing images from flickr. Images on Flickr tagged with “route 66” Images tagged with “postcard”
Image retrieval The first microcomputer-based image database retrieval system was developed at MIT, in the 1990s, by Banireddy Prasaad, Amar Gupta, Hoo-min Toong, and Stuart Madnick.[1] A 2008 survey article documented progresses after 2007.[2] Search methods[edit] Image search is a specialized data search used to find images. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images "similar" to the query. The similarity used for search criteria could be meta tags, color distribution in images, region/shape attributes, etc. Image meta search - search of images based on associated metadata such as keywords, text, etc.Content-based image retrieval (CBIR) – the application of computer vision to the image retrieval. Data Scope[edit] It is crucial to understand the scope and nature of image data in order to determine the complexity of image search system design. Evaluations[edit] See also[edit] References[edit]
Image Recognition and Visual Search Le droit aux images à l'ère de la publication électronique - Actualités de la recherche en histoire visuelle En février 2005, le portail Persée, spécialisé dans l'édition numérique rétrospective, ouvrait ses colonnes au public [1]. Parmi les collections disponibles en libre accès figurait une cinquantaine de numéros de la Revue de l'art (1988-1999). Un sort particulier avait été réservé à la plus prestigieuse publication française du domaine. Au lieu de l'abondante illustration accompagnant les numéros papier, les pages en ligne arboraient de vastes espaces blancs, des légendes renvoyant à des cadres vides. Aurait-on admis de voir une revue de littérature dépouillée de ses citations, une revue de mathématiques caviardée de ses équations? Nul ne s'interroge alors sur le symptôme inquiétant que représente une revue d'histoire de l'art débarrassée de l'objet même de ses travaux: son iconographie. Devant le durcissement du dispositif légal concernant la publication sur internet, la rédaction de la revue Etudes photographiques prenait en juin 2006 la décision de renoncer à son édition en ligne[2].
How to Search Images Online –Search Engine Journal Image search is evolving rapidly. Today the machine understands much more about images than just a year ago: it can read the text on the image, see its colors and classify it based on its form, shape and textures. So which advanced image search methods can we use today? Image search based on image content (face / landscape / photo / product image search) Face search has been a hot topic recently. Exalead has become known for integrating facial recognition technology but it still lacks some accuracy. Image search based on color Picitup allows to set color preferences (choose among 18 colors to set the search dominating palette);PicSearch and Snap.com recognize between colorful and black-and-white images.Etsy searches only inside its own product database but its color-based search engine is both fun and pleasure to play with. Image search based on similarity Recently launched Tineye.com (registration required) searches for similar images online.
Similar Images graduates from Google Labs Today, we're happy to announce that Similar Images is graduating from Google Labs and becoming a permanent feature in Google Images. You can try it out by clicking on "Find similar images" below the most popular images in our search results. For example, if you search for jaguar, you can use the "Find similar images" link to find more pictures of the car or the animal. When we revamped Labs in April, we also launched Similar Images to highlight some of the innovative work our engineers have been working on. Google Labs gives us a way to get some of our new ideas in front of you early in the process, refine them based on your feedback and see what sticks. So, let's say you want to find images of Ancient Egypt. Or illustrative maps of Ancient Egypt: Or ancient Egyptian-style drawings:
McGreevey Collection : un album sur Flickr Michael T. McGreevey, saloon owner and baseball fan, lived the classic American success story. Born the son of an Irish immigrant day-laborer, McGreevey opened his first bar in 1894. His establishment soon became the headquarters of the Boston Royal Rooters, the rabid and riotous fans of Boston’s professional teams, the Boston Nationals, also known as the Braves, and the Boston Americans, later to be known as the Red Sox. But with the passage of the 18th Amendment prohibiting the sale of alcohol and inaugurating the Prohibition Era, McGreevey was out of business. The collection consists of early Boston baseball photographs dating from 1875 to 1916. Special thanks to Glenn Stout for his extensive research into the life of Michael T. For more information please contact: Print Department McKim Building, 3rd Floor Boston Public Library 700 Boylston Street Boston, MA 02116 617-859-2280 or 617-536-5400 www.bpl.org/research/print/index.htm
PixID Image Monitoring Service - Idée Inc. - The Visual Search Company PixID Editorial Image Tracking Identify editorial images in print publications. “Idée’s image monitoring service for print and web is an outstanding technological breakthrough.” - Michael Scotto, Director Photo Business, Agence France-Presse (AFP) Request a Demo Benefits of Image Tracking with PixID Automate your editorial billing process Recover license revenues from unaccounted image use Uncover unauthorized image use Accurately identify every image usage Track authorized and exclusive image use Verify image license compliance Introduce accountability to your distribution relationships Streamline royalty disbursements to your photographers Determine ROI and highlight opportunities for increased sales in new markets Back up content purchasing decisions with solid, market–relevant data PixID Image Monitoring Service A technological breaktrough in image identification and tracking for print publications. How PixID Image Monitoring Works Who should use PixID?
Lire | SemanticMetadata.net An Open Source Java Content Based Image Retrieval Library The LIRE (Lucene Image REtrieval) library provides a simple way to retrieve images and photos based on their color and texture characteristics. LIRE creates a Lucene index of image features for content based image retrieval (CBIR). Several different low level features are available, such as MPEG-7 ScalableColor, ColorLayout, and EdgeHistogram, Auto Color Correlogram, PHOG, CEDD, JCD, FCTH, and many more. Furthermore simple and extended methods for searching the index and result browsing are provided by LIRE. LIRE scales well up to millions of images with hash based approximate indexing. Documentation and help are available Please drop me a line on the mailing list if you use LIRE in your project. If you like this work and you want to support the development of Lire, consider to donate to the project. Consulting & Services You plan to work with LIRE and you are stuck? Which ones are the right features for me? . Lux Mathias, Savvas A.