Image Retrieval Using Spatial and Semantic Information

MPhil Thesis Defence


Title: "Image Retrieval Using Spatial and Semantic Information"

By

Miss Jie ZHOU


Abstract

With the pervasiveness of online multimedia content on the web, content-based 
image retrieval (CBIR) has attracted more and more interest in recent years due 
to the unsatisfactory performance of conventional concept-based image retrieval 
techniques based on text-based annotations. Two promising CBIR approaches are 
based on bag-of-words (BoW) models and topic models (such as the latent 
Dirichlet allocation, or LDA, model) as inspired by their success in text-based 
information retrieval applications. However, BoW models do not consider the 
spatial relationships and latent semantic relationships between words. Even 
though topic models take into consideration the semantic information in 
documents, they still ignore the spatial information. Recent years have seen 
the emergence of new methods which attempt to remedy the shortcomings of these 
two approaches. This thesis starts with a review of recent CBIR approaches that 
incorporate spatial and semantic information. With this as background, we 
propose two methods to combine the LDA model with spatial information. The 
first method, referred to as BoP+LDA, combines an extension of the BoW 
representation called bag-of-phrases (BoP) with the LDA model. The second 
method, called ssLDA, incorporates image segmentation into the LDA model and 
re-ranks the retrieved images by exploiting topic spatial consistency. We 
empirically compare our proposed methods with some baseline methods on 
real-world image data. From the experimental results, we conclude that 
incorporating both spatial and semantic information is effective in improving 
the image retrieval performance.


Date:			Tuesday, 25 June 2013

Time:			10:00am – 12:00noon

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Prof. Dit-Yan Yeung (Supervisor)
 			Prof. Nevin Zhang (Chairperson)
 			Dr. Albert Chung


**** ALL are Welcome ****