<p>There are some literature in that area:</p>

<p><a href="http://interscience.in/IJEEE_Vol1Iss3/Paper7.pdf" rel="nofollow">Content Based Image Retrieval Using SVM Algorithm</a></p>

<p><a href="http://link.springer.com/chapter/10.1007/978-1-4614-3872-4_37?no-access=true" rel="nofollow">An Approach for Image Retrieval Using SVM </a></p>

<p><a href="http://www.cs.sfu.ca/~mori/research/papers/lan-eccv12.pdf" rel="nofollow">Image Retrieval with Structured Object Queries Using Latent Ranking SVM </a></p>

<p>As far as I know, the simple approach is having a feature extraction phase (i.e Using PCA) and then doing a <a href="http://rvlasveld.github.io/blog/2013/07/12/introduction-to-one-class-support-vector-machines/" rel="nofollow">one-class svm</a> classification.</p>

<p>K-NN usually uses euclidean distance anyway so the algorithm is offering you a more consistent decision boundary and a feature extraction phase on top of that. You can see an example <a href="http://arxiv.org/abs/1307.4717" rel="nofollow">here</a></p>
How does CBIR work with SVM instead of Euclidean distance?
<p>Generally CBIR works with Euclidean distance for comparing a query image and a database image feature vectors.</p>

<p>However in math works, I got a source code that instead of Euclidean distance it is done with SVM, like a content based image retrieval using two techniques:</p>

<ol>
<li>Using knn for image retrieval;</li>
<li>Using svm for image retrieval.</li>
</ol>

<p>How does it work?</p>