Elasticsearch Based Image Search Using RGB Signatures
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Elasticsearch Based Image Search Using RGB Signatures
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Re: Elasticsearch Based Image Search Using RGB Signatures
#2I wonder if pHash wouldn't make this a lot more effective. Anyone tried building an ES-usable distance function for pHash?
Re: Elasticsearch Based Image Search Using RGB Signatures
#3For anyone interested in the computer vision side of this topic, the author here is using a variant of color histograms, which was state of the art around 1990 [1][2]. Since 2003, bag of visual words approaches have usually meant extracting SIFT-like features from a database of images, quantizing the features down to a list of thousands or millions of "words", and then treating the images like documents containing those "visual words" [3][4]. (Nothing wrong with the approach he's using [simple and fast], but the bag of words terminology in the article usually suggests a different class of approaches.)
[1] https://staff.fnwi.uva.nl/r.vandenboomgaard/IPCV/_downloads/...
[2] https://www.cs.utexas.edu/users/dana/Swain1.pdf
[3] http://www.robots.ox.ac.uk/~vgg/publications/papers/sivic03....
[4] http://www-inst.eecs.berkeley.edu/~cs294-6/fa06/papers/niste...
Re: Elasticsearch Based Image Search Using RGB Signatures
#4For anyone interested in the computer vision side of this topic, the author here is using a variant of color histograms, which was state of the art around 1990 [1][2]. Since 2003, bag of visual words approaches have usually meant extracting SIFT-like features from a database of images, quantizing the features down to a list of thousands or millions of "words", and then treating the images like documents containing th…
Forgive my ignorance, but is there something like word2vec but for images- like an image2vec? In terms of text processing, word2vec is one of the best approaches. So can't you describe the features of images using neural networks and then vectorize them using word2vec?
Its been acouple years since my computer vision course (was my favorite course in university) but isn't SIFT a bit '99? Aren't there better methods now such as neural networks for feature description?
Re: Elasticsearch Based Image Search Using RGB Signatures
#5Really cool. But maybe its just me, aren't butterflies kind of hard to distinguish between each other. I feel as though his search result page- i couldn't really tell if it was good or not because they all looked kind of similar shape-wise. Only difference is color and even then its fairly little color.
Re: Elasticsearch Based Image Search Using RGB Signatures
#6Another open-source implementation using a similar concept and Elasticsearch is image-match: https://github.com/ascribe/image-match
Re: Elasticsearch Based Image Search Using RGB Signatures
#7Really cool. But maybe its just me, aren't butterflies kind of hard to distinguish between each other. I feel as though his search result page- i couldn't really tell if it was good or not because they all looked kind of similar shape-wise. Only difference is color and even then its fairly little color.
The strategy does seem to assume a highly normalized set of images that only differ in color and contour.
Re: Elasticsearch Based Image Search Using RGB Signatures
#8For anyone interested in the computer vision side of this topic, the author here is using a variant of color histograms, which was state of the art around 1990 [1][2]. Since 2003, bag of visual words approaches have usually meant extracting SIFT-like features from a database of images, quantizing the features down to a list of thousands or millions of "words", and then treating the images like documents containing th…
Forgive my ignorance, but is there something like word2vec but for images- like an image2vec? In terms of text processing, word2vec is one of the best approaches. So can't you describe the features of images using neural networks and then vectorize them using word2vec? Its been acouple years since my computer vision course (was my favorite course in university) but isn't SIFT a bit '99? Aren't there better methods no…
I'm also pretty ignorant here, but if I understand word vectors correctly they are a trained model that results in the ability to predict the next word in a sequence. I can imagine extending that idea to images, i.e. in such a way that the color of a target pixel can predict the colors of surrounding pixels. I wonder if tools like Photoshop and Gimp don't already have similar algorithms for some advanced effects. In any case, it seems several layers of abstraction down the stack from the problem of attaching meaning to images. Isn't that more what the BOW approach tries to do?
Re: Elasticsearch Based Image Search Using RGB Signatures
#9For anyone interested in the computer vision side of this topic, the author here is using a variant of color histograms, which was state of the art around 1990 [1][2]. Since 2003, bag of visual words approaches have usually meant extracting SIFT-like features from a database of images, quantizing the features down to a list of thousands or millions of "words", and then treating the images like documents containing th…
Forgive my ignorance, but is there something like word2vec but for images- like an image2vec? In terms of text processing, word2vec is one of the best approaches. So can't you describe the features of images using neural networks and then vectorize them using word2vec? Its been acouple years since my computer vision course (was my favorite course in university) but isn't SIFT a bit '99? Aren't there better methods no…
Images don't necessarily map directly onto a word2vec-like solution but the closest thing is pre-trained deep networks, for example:
http://caffe.berkeleyvision.org/model_zoo.html
You're right that bag of words with SIFT is not state of the art, with deep learning dominating computer vision approaches these days.
Re: Elasticsearch Based Image Search Using RGB Signatures
#10If you're interested in searching photos with ES - I took a some what simpler approach focusing just on major colors in the image.
But with some of the machine vision API google cloud etc. you could extend to other "features"
Details: http://blog.sandeepchivukula.com/posts/2016/03/06/photo-sear...