Interesting news from LibraryThing:
LibraryThing: A million free covers from LibraryThing
A few days ago, just before hitting thirty million books, we hit one million user-uploaded covers. So, we’ve decided to give them away—to libraries, to bookstores, to everyone.
Excellent stuff. We’ll have to see if we can get a key 
Dave Pattern up in Huddersfield has reminded me that he has already done some work on identifying colours in book jackets and then retrieving them to good effect. Now I have been prompted, I vaguely remember having looked at Dave’s work. Yet when Richard suggested we could do something interesting with book jackets… nope… blank… Thanks dave for reminding me! I see also that Ed Vielmetti has spotted us at work… Ed, a bit of the back story can be found on the Colourphon Wiki 
.
Of course this little project is in very early days - we are not even storing anything yet!! - so come back later and see how we are getting on. And of course, If you have any ideas or things that you think might be rather cool applications - let us know, either here, or on the aforementioned Wiki.
Tonight I added support for Exif data capture from the image (if it is a JPEG). We will need to map this to something useful, but I have already found a schema and a potential description vocabulary.
Try this example.
We were figuring that it might be useful to have a colour match that was in some way relevancey ranked, so I have been working on ways to achieve this.
What we have now is a result set that is sorted according to the position of the frequent colours in the image. We also know where each cell is in the image, and can calulate - at it’s simplest - a sort order based on centre weighting.
Need an example? These examples will take a moment or two to calculate…
Try this one: Test number one.
Or this one: Test number two.
We divide the image into an odd number of cells, ensuring that there is one in the middle. We then scan each cell and analyse the colour content. Then we give you the results.
Simple yet strangely satisfying, and not only that, but you can analyse an image from any source, be it File, URL or book jacket retrieved by isbn search courtesy of the Talis Platform.
After some early prototypes, proof of concepts and all round “learning” experience Tim and I have overhauled all that we have written and kept all functionality. My Uni days are now long gone, with every bit of 5 minute code at prototype easily taking 15 minutes to perfect…
Anyway, Tim has worked hard and put in some groovy functions, which in all means what we originally set out to do is *almost* complete - with one major flaw - we’re not *yet* storing anything.
This was our original problem, mulled over lunch whilst walking around the Business Park …
So, someone walks into a library and says … ‘I saw this book last week, in the history section and it was blue … do you know where it is now?’
The librarian replies ‘Do you know the Author, or the Title?’
‘No’ said the *now* frustrated customer…
Ah - but if we could somehow harvest the colours of a book, store them and allow users to search against them …
And this is how we were born!
Wouldn’t it be great if you could submit an ISBN, and have Colourphon go away and find a jacket image for you?
Well now you can!
Try this out now! and if you can’t find an ISBN to test, try this one: 0764555871
Yeay! Fixed a rather niggly bug.
Displayed colour counts were inaccurate, although the displayed colours were correctly identified as being the most prevalent in the image. This was a frustrating bug, as it meant that calculation of ‘percentage of image of particular colour’ could not be reliably calculated. I mean, I am looking at 36000 odd pixels, and the top colour is reported to have occured 12 times. hmmm.
The bug was mainly caused by an incorrect comparison, which assumed that all colours in the incoming array were repeated sequentially rather than randomly. All I had to do was check for existence of the key, rather than comparing the current value to the previous value. Voila.
So we are pretty happy with the colour guessing now, although at this stage we are limited to a named palette of 254 colours. Try it.
The guessing is based on a sample of the most frequently occurring 400 colours, that are then compared to each other to match those that are within 10%, then de-duplicated at the named colour stage. This seems to give a fairly accurate representation of the main colours in the image. Next stage is capturing that info in some sort of data model. So we are looking into the semantic web’s best friend RDF.
This week has been mostly about learning, although it is worth mentioning that we have got better at analysing a particular image, and you also get a best guess shade, and the colour that that shade belongs to.
A few of the blogs we follow brought up something related that could be very useful in the future of colourphon - namely this article about HTML imagemaps. Something like this could be used to give focus on the images that we analyse - meaning that a more accurate colour can be obtained - the development experiments have begun - but confined to localhost (I’m afraid).
Our main aim is to help build the semantic web with images - our focus on just getting the data at the moment, but with the potential of harvest and storage (RDF) in the future. We found this which really helped me understand some of the simpler parts of RDF (triples etc… I told you we were learning this week :D).
We can now use images from an upload and from a remote URL source. Images are created using a truecolour palette - optionally, we can use a indexed colour palette (defaults to 256) - currently this is not user configurable
Now I am off for a wee Christmas break.
