Re-ranking for web image search
With the rise of smartphone enabled camera, people are taking more photos because of the ease of convenience of pointing and shooting. With more photos being taken, there would be an increase in image uploads on image sharing website which causes searching for relevant images in this la...
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Format: | Final Year Project |
Language: | English |
Published: |
2014
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Subjects: | |
Online Access: | http://hdl.handle.net/10356/59131 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | With the rise of smartphone enabled camera, people are taking more photos because of the
ease of convenience of pointing and shooting. With more photos being taken, there would be
an increase in image uploads on image sharing website which causes searching for relevant
images in this large database of images more difficult.
In this project, the aim is to create a new image re-ranking framework based on Text-based
information retrieval (TBIR). The proposed framework works in 3 stages, the initial ranking
stage, Weak Bag Annotation Stage and the SVM training and prediction stage.
In the initial ranking stage, images are given a ranking score based on their surrounding tags
and the position of the tag that the search query matches. In the weak bag annotation stage,
images are clustered together based on their textual and visual feature. The clusters are
treated as bags where only the bags are labelled while the images within the bags are
unknown hence this is a weak bag annotation. Using this bags, the SVM model is trained and
using the model, a label is given to each image of the queried images based on the prediction
of the model. The images are then finally re-ranked based on their ranking score.
The experiments will be conducted on the real world NUS-WIDE dataset to display the
effectiveness of the proposed framework. |
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