Image Analysis - Orange - Clustering - Key Findings I

After doing the presentation on Image Analysis from Orange at the last Sri Lanka Data Community, I thought of explaining a few interesting findings from a few upcoming of blog posts. 

Let us look at CLustering techniques. As you know clustering is natural grouping. Let us look at images in a folder without any grouping. You will see some data set that was download from the internet by a third party so that this dataset is semi-unbias data set. 

Sample Image Set

Then in Orange, we have done the following configuration in order to make clustering.

Configuring of Clustering in Orange Tool for Image Processing

In the Image Embedding, Inception V3 is used and Embedder and Cosine Distance is used as the distance measurement in Distances control.

Now let us look at natural clustering outcomes by selecting different clusters in Hierarchical Clustering.

Birds Cluster

Here is another cluster with Flowers. 

Flower Cluster

There are more some interesting clusters like below. 

So this is a very interesting grouping. Though not all the groups have the same images, this will provide you with an interesting image clustering.