Right now the K-means clustering method is used to find K distinct colors from the album artwork. This method has its flaws, more specifically:
- Need to know K (how many distinct colors the image have)
- Always learns convex clusters
- Sensitive to outliers
Therefore, the accuracy of the system could potentially be significantly increased by changing the clustering method. Potential clustering methods include DBSCAN and HDBSCAN, where the latter probably is the better choice as it allows clusters of different densities, is efficient and only requires one parameter, namely the minimum cluster size.
The clustering takes place in the file spotify_background_color.py in the method best_color. When evaluating different clustering methods, the plotting feature of this method might be of use. This can be activated by simply setting the argument plot=True.
Right now the K-means clustering method is used to find K distinct colors from the album artwork. This method has its flaws, more specifically:
Therefore, the accuracy of the system could potentially be significantly increased by changing the clustering method. Potential clustering methods include DBSCAN and HDBSCAN, where the latter probably is the better choice as it allows clusters of different densities, is efficient and only requires one parameter, namely the minimum cluster size.
The clustering takes place in the file
spotify_background_color.pyin the methodbest_color. When evaluating different clustering methods, the plotting feature of this method might be of use. This can be activated by simply setting the argumentplot=True.