A comparative study on a practical use case for image clustering based on common shareability and metadata

Detta är en Master-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: As the amount of data increases every year, the need for effective structuring of data is a growing problem. This thesis aims to investigate and compare how four different clustering algorithms perform on a practical use case for images. The four algorithms used are Affinity Propagation, BIRCH, Rectifying Self-Organizing Maps, Deep Embedded Clustering. The algorithms get the image metadata and also its content, extracted using a pre-trained deep convolutional neural network. The results demonstrate that while there are variations in the data, Affinity Propagation and BIRCH shows the most potential among the four algorithms. Furthermore, when metadata is available it improves the results of the algorithms that can process the extreme values cause. For Affinity Propagation the mean share score is improved by 5.6 percentage points and the silhouette score is improved by 0.044. BIRCH mean share score improves by 1.9 percentage points and silhouette score by 0.051. RSOM and DEC could not process the metadata. 

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