Abstract:
The proposed content-based ranking algorithm for image retrieval uses the
visual features of images (visual words) to retrievemorerelevant images. Hence,
making the retrieval process moreaccurate than the tag-based Image Retrieval
(TBIR).
Initially, the system creates a visual vocabulary and trains a classifier on a
dataset of 2,400 images belonging to different categories.Next, for any given userquery,
the system makes a decision to display a class of images that best matches the
query. These class images are then processed in a way that we compute the relevance
scores for each image and display the result based on the score.
Our content-based ranking algorithm is then integrated with the tag-based
ranking algorithm (Module-I).Both tag and content-based image retrieval techniques
have their own advantages and disadvantages. By integrating them together, some of
their disadvantages can be overcome. The existing image search engines are either
tag-based or content-based, but not both. Thus, a new system with these techniques
integrated together is highly needed.
