Abstract:
The proposed tag-based ranking algorithm for image retrieval uses both the
meta-data of images and visual features of images to retrieve the most relevant result set.
Hence, making the retrieval of images more accurate than the tag-based and contentbased
image retrievals.
Initially, the system extracts meta-data of images and stores them into a specially
designed dictionary dataset. Then, given a textual query, the system computesdifferent
relevance scores on the basis of user tags and content tags (provided to us from Module-
II). Next, we combine the scores from both user tags and content tags to compute a final
list of scores.
At the end, we rank our images, which are related to the user query and display
the result based on the new list of scores.
