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Searching by Image Instead of Keywords
Content based image retrieval (CBIR), the technique to search for images not by keywords, but by comparing features of the images themselves has been the focus of much research ever since the web emerged. Consider for instance adding CBIR to Google Images, where you would be able to search for images similar to a query image instead of using keywords. A research project at Penn State University has recently been applied to the biggest aviation photo database in the world with close to 800,000 images. You can search for images similar to a photo already in their database (click "View similar photos") or submit your own query image. Some queries generate better results than others but CBIR is certainly here to stay and will be standard in many image applications of the future.
I can't wait to put a nipple into it!
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(O.o) This is Bunny. (> <)
What an awful beach.
After all, I am strangely colored.
I was just thinking about this the other day. I think content-based image search is one of the Next Big Things. Cameras are so ubquitous now (for better or worse), but having to rely on metadata to give meaning to images requires lots of effort up front.
It will be interesting if we ever get to a stage where we can just search for a random object (or person) in a database of photos. Then you could take pictures of everything with an always-on camera and if you need to find where you put your car keys, just do a search.
This is just asking for trouble. As most of you would probably imagine, some self-proclaim "comdeian" would post either porn pictures, or pictures that resembles porn body position.
;)
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They would need a team of outsource Indian workers to go through each picture one by one!
I am not Indian but...can I apply for the image filtering job?
I said this first, I should get the job
Because it still has problems - you'll note that the pictures seem to be compared simply based on color similarity. That's the same thing imgSeek does (a great program for sorting and searching your photos) on photo searches. It works wonderfully if you're searching a very limited picture subset (say, airplanes), but if you search a wide variety of pictures, the results can be quite amusing.
It's a Cyrillic alphabet. It's like all those keys you never push on a calculator.
Something with two circles and dots in the middle of each circle.
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(O.o) This is Bunny. Add Bunny to your signature
(> <) to help him achieve world domination.
... the search engine will support ASCII art image searches.
Vintage computer adverts: http://www.vintageadbrowser.com/computers-and-software-ads
There's a bunch of interesting papers out there on content-based image analysis and retrieval. Below is a sampling from my bibtex file. Does anyone else have others they'd like to share?
* Finding Naked People (Fleck et al, 1996)
* Video google: A text retrieval approach to object matching in videos (Sivic & Zisserman, 2003): web page demo here
* Names and Faces in the News (Berg et al, 2004)
* FACERET: An Interactive Face Retrieval System Based on Self-Organizing Maps (Ruiz-del-Solar et al, 2002)
* Costume: A New Feature for Automatic Video Content Indexing (Jaffre 2005)
I forgot one more, where specific faces were automatically retrieved from feature-length movies and Fawlty Towers:
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films (Arandjelovic & Zisserman, 2005)
The objective of this work is to recognize all the frontal faces of a character in the closed world of a movie or situation comedy, given a small number of query faces. This is challenging because faces in a feature-length film are relatively uncontrolled with a wide variability of scale, pose, illumination, and expressions, and also may be partially occluded. We develop a recognition method based on a cascade of processing steps that normalize for the effects of the changing imaging environment. In particular there are three areas of novelty: (i) we suppress the background surrounding the face, enabling the maximum area of the face to be retained for recognition rather than a subset; (ii) we include a pose refinement step to optimize the registration between the test image and face exemplar; and (iii) we use robust distance to a sub-space to allow for partial occlusion and expression change. The method is applied and evaluated on several feature length films. It is demonstrated that high recall rates (over 92%) can be achieved whilst maintaining good precision (over 93%).
Talking about greatness to society and a little bit of skin. At university one of my projects was a system that used CBIR to try and diagnose skin cancer. The doctor would take an image of the suspect area it then would be compared against a database of cancers. It would then return a suggested likelyhood of being cancer. It also allowed the doctor to build a history of images allowing easy comparision over time.
I always felt good about working on projects like this, gives a warm fuzzy feeling.
One has to guess the search word which generated a given set of 20 images in google's image search
When things are moving forward, we have soomthing to talk about "those good ole days" but frankly the game is interesting initially but later gets boring due to the frequent repetitions..
What I got was an awful lot of red planes - some of which were actually Qantas planes, but I think more by coincidence (i.e., they're red) than design. Many images had nothing to do with Qantas, or even a red plane - they simply had a lot of red in the image.
This is impressive in some ways, but in others it seems like it's simply looking for similar patches of colour. I haven't done enough testing to see what happens if,say, I gave it a half red half green image.
Interesting, but not ready for public consumption just yet. A bit like A.L.I.C.E. the artifial intelligence system actually - neat, but not practical. Yet!
Physicist, consultant, science communicator
Now I can find all the other naked pictures of Bea Arthur on the web!
The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS). It enables you to do Query By Example on images, giving you the opportunity to improve query results by relevance feedback. For processing your queries the program relies entirely on the content of the images, freeing you from the need to annotate all images before querying the collection.
GIFT It worked pretty well for me in the demos they linked too. I have been waiting for this type of application to gain momentum.
Wax on, wax off baby!
Oh, you mean like imgseek?