Congratulations to Xiaqing Pan for Passing His PhD Defense
Congratulations to Xiaqing Pan for passing his defense on January 23, 2016. His Ph.D. thesis is entitled “Machine Learning Methods for 2D/3D Shape Retrieval and Classification”.
Abstract of thesis:
Shape classification and retrieval are two important problems in both computer vision and computer graphics. A robust shape analysis contributes to many applications such as manufacture components recognition and retrieval, sketch-based shape retrieval, medical image anaysis, 3D model repository management, etc. In this dissertation, we propose three methods to address three significant problems such as 2D shape retrieval, 3D shape retrieval and 3D shape classification, respectively.
First, in the 2D shape retrieval problem, most state-of-the-art shape retrieval methods are based on local features matching and ranking. Their retrieval performance is not robust since they may retrieve globally dissimilar shapes in high ranks. To overcome this challenge, we decompose the decision process into two stages. In the first irrelevant cluster filtering (ICF) stage, we consider both global and local features and use them to predict the relevance of gallery shapes with respect to the query. Irrelevant shapes are removed from the candidate shape set. After that, a local-features-based matching and ranking (LMR) method follows in the second stage. We apply the proposed TSR system to three shape datasets: MPEG-7, Kimia99 and Tari1000. We show that TSR outperforms all other existing methods. The robustness of TSR is demonstrated by the retrieval performance.
Second, a novel solution for the content-based 3D shape retrieval problem using an unsupervised clustering approach, which does not need any label information of 3D shapes, is presented. The proposed shape retrieval system consists of two modules in cascade: the irrelevance filtering (IF) module and the similarity ranking (SR) module. The IF module attempts to cluster gallery shapes that [...]