Flask based REST application for Solr Elasticsearch similarity using Jaccard, Edit-distance, Cosine & K-means metrics

Persists data onto a Document Store, which is later Clustered & Visualized in D3

1.Mount your raw images & Start Solr Instance

cd /path/to/images/directory/
python -m SimpleHTTPServer

2.Source the below Env Variable

export IMAGE_MOUNT=http://localhost:8000/

3.Start the Flask application

cd /path/to/Solr-ES-Similarity
python solr-similarity.py

4.Open the concerning D3 Viz REST endpoints in your browser

####Jaccard Metadata Key http://localhost:5000/static/dynamic-cluster.html

####Jaccard Metadata Value http://localhost:5000/static/dynamic-cluster-value.html

####k-means http://localhost:5000/static/dynamic-cluster-kmeans.html

###Thresholding

Try to cluster pairwise similarity scores (based on Jaccard(wrt Golden Feature Set), Edit, Cosine distance) by setting a threshold value

Number of clusters that will be found is Not known Apriori

Applicable D3 Viz = dynamic cluster, circlepacking

###Clustering

Using a metadata feature, such as string length, Cluster documents represented in N dimensional feature space as Vectors using Euclidean distance.

Specify the number of clusters to find Apriori

1.k-means clustering for absolute distance scores

Applicable D3 Viz = dynamic cluster

2.shared Nearest Neigbor clustering for pairwise similarity scores

Applicable D3 Viz = dynamic cluster, circlepacking with tooltips