MSc Thesis Defense Announcement of Eisa Adil: "Content-Based Image Retrieval using Hierarchical Decomposition of Feature Descriptors "

Wednesday, October 13, 2021 - 10:00 to 12:00


The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Eisa Adil 

Date: Wednesday October 13th, 2021 
Time:  10:00am – 12:00pm 
Passcode: If interested in attending this event, contact the Graduate Secretary at with suffient notice before the event to obtain the passcode


Due to modern technological advancements, the pervasiveness and complexity of images have remarkably increased. Searching databases for similar visual content, i.e., Content-Based Image Retrieval (CBIR), remains an open research problem. In this thesis, we propose a novel CBIR approach, in which each symbolic image has a quadtree representation consisting of SIFT-based orientational keypoints. Every quadrant node in the tree represents the dominant orientation of a region in the image. The quadtree image representation is used for bitwise signature indexing and image similarity measurement. Also, we convert each quadtree image representation to a trainable feature vector for use in the K-Nearest Neighbour algorithm and Siamese Deep Neural Networks. The proposed approaches are evaluated using mean average precision (mAP), precision, recall, f-score and contrastive loss on three different image datasets. Our results indicate that, for complex images, orientational quadtrees are significantly more accurate than spatial quadtrees. Further, the derived feature vectors can be used in other machine learning or deep learning methods for training, ensemble, boosting, aggregation or embedding. 
Keywords: CBIR, Image-retrieval, Similarity detection, Quadtree, SIFT, Feature Descriptor, K-Nearest Neighbours, Siamese Networks 

MSc Thesis Committee:  

Internal Reader: Dr. Boubakeur Boufama              
External Reader: Dr. Myron Hlynka 
Advisor: Dr. Imran Ahmad 
Chair: Dr. Saeed Samet 

 MSc Thesis Defense Announcement

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