This is really awesome. For anyone who wants a naive, poor man's implementation of this, I have an unfinished Ruby gem that's a good starting point for you: <a href="https://github.com/JoshSmith/kaleidoscope" rel="nofollow">https://github.com/JoshSmith/kaleidoscope</a><p>Here's how I got it to work (cribbed from my own README):<p>TL;DR: I used k-means clustering to segment a database of images into color bins for quick searching.<p>Using imagemagick, I ran histograms on images and converted their top n most frequent colors into L<i>a</i>b* color space for an approximate representation of human vision.<p>Colors were then matched to a user-defined set of colors using Euclidean distance, i.e. a "bin". I could choose any array of RGB values of arbitrary length.<p>I then stored hexadecimal values of the image's original color and the matched color, along with the frequency of that color within the image (for sorting based on frequency) and the Euclidean distance (for sorting by tolerance).<p>Then finding images close to a certain color was as simple as Photo.all.with_color('#993399') and order by frequency and Euclidean distance. Here's a photo of the results: <a href="https://github-camo.global.ssl.fastly.net/89cc87ac84cd3a1d1223e8f9d560e65eb8447ef6/687474703a2f2f636c2e6c792f696d6167652f336e3243313631373069306b2f53637265656e25323053686f74253230323031332d30322d30352532306174253230362e35362e3434253230504d2e706e67" rel="nofollow">https://github-camo.global.ssl.fastly.net/89cc87ac84cd3a1d12...</a><p>I might spend some time reverse-engineering Shutterstock's implementation, since it sounds way better than mine and clearly works at scale. But for my purposes, my own implementation worked just fine.<p>If you want help implementing it, feel free to reach out to me!
Reminds me of TinEye's multicolr demo [0] that searches through CC-licensed images on Flickr. Their multiple color feature was really nice (however they don't have shuttershock's keyword filtering.)<p>I wonder if anyone ever bought TinEye's color-search-engine-as-a-service [1]. The as-a-service model seems really awkward for something that requires so much integration, and this new shuttershock feature (developed from the ground up) seems to confirm this.<p>[0] <a href="http://labs.tineye.com/multicolr" rel="nofollow">http://labs.tineye.com/multicolr</a><p>[1] <a href="http://services.tineye.com/MulticolorEngine" rel="nofollow">http://services.tineye.com/MulticolorEngine</a>
I think the next step for them is to cluster by similar images. A green hue, for instance, shows a lot of similar-looking pictures of leaves—better to show one picture and have a "show similar images" feature to dig into a finer level of variance.<p>V. cool, though.
Airliners.net had something similar since 2005.<p><a href="http://www.airliners.net/similarity/" rel="nofollow">http://www.airliners.net/similarity/</a><p>It was on Slashdot back then: <a href="http://tech.slashdot.org/story/05/05/04/2239224/searching-by-image-instead-of-keywords" rel="nofollow">http://tech.slashdot.org/story/05/05/04/2239224/searching-by...</a>
Is somebody thinking on applying this to a different stock photo model? I mean, I have heard shutterstock takes a big cut and pays very little to photographers. Nobody out there with these innovative ideas but with a business plan more friendly for photographers?
The three sliders here correspond to averages of the three LCH channels. Has anyone thought of looking into the second moments of those? As in variances and covariances in order to get high contrast in luminance or high contrast in hue…
"the prototype had over 20 sliders to control all the visual attributes"<p>I cannot read the slider labels, the screenshot is very low-res :(