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