A Novel Approach for Ranking Images Using User and Content Tags

In this study, a tag and content-based ranking algorithm is proposed for image retrieval that uses the metadata of images as well as the visual features of images, also known as "visual words" to retrieve more relevant images. Thus, making the retrieval process more accurate than the keyword-based retrieval approaches. Both tag and content-based image retrieval techniques have their own advantages and disadvantages. By combining the two, their disadvantages have been offset. The proposed system has been developed to bridge the gap between the existing techniques and the desired user requirements. Initially, the system extracts the metadata of images and stores them into a custom designed dictionary dataset. Then, the system creates a visual vocabulary and trains a classifier on a dataset of images belonging to different categories. Next, for any given user-query, the system makes a decision to display a class of images that best matches the query. These class images are processed in a way that we compute the relevance scores for each image and display the result based on the score.