Dining “Where's the closest coffee shop?” “Show me my dinner reservations for Thursday.” “Show me the menu for Masa 14.” “How many calories are in a double cheeseburger?” Movies “What movies are playing tonight?” “Show me the trailer for Furious 7.” “When does Minions come out?” “Where can I see Unbroken around here?” Music “Play Modest Mouse.” “What's this song?” “Show me my email from Brian about Modest Mouse.” “What are songs by Ziggy Stardust?” Sports “Did the Lakers win today?” “What NBA games are on tonight?” “Who does Carmelo Anthony play for?” “How many titles has Tim Duncan won?” Find artist and song information instantly.
It's easy to search for content, even if it's in another app. Travel “When should I leave for the airport today?” “Is my flight delayed?” “How much is 10 dollars in Euros?” “How do you say let's go out tonight in French?” Take Action “Call Jake.” “Text Taylor, be there in 10, can't find my keys.” 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. Your support has helped to make Similar Images the first major feature to graduate from Google Labs since its recent overhaul.
So, let's say you want to find images of Ancient Egypt. Or illustrative maps of Ancient Egypt: Or ancient Egyptian-style drawings: Pictures. Sepham - Search by Drawing. Category:Image search. Content-based image retrieval. General scheme of content-based image retrieval 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 techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a recent scientific overview of the CBIR field).
Content-based image retrieval is opposed to traditional concept-based approaches (see concept-based image indexing). History The term "content-based image retrieval" seems to have originated in 1992 when it was used by T. Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features.
Technical progress CBIR techniques Query techniques Color List of CBIR engines. Elastic Vision - Content-based Image Search. TinEye Reverse Image Search. Visual Search Lab - Idée Inc. BYO Image Search Lab - Idée Inc. 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. 百度识图——相同图片搜索.