Instance search with weak geometric correlation consistency

Finding object instances from large image collections is a challenging problem with many practical applications. Recent methods inspired by text retrieval achieved good results; however a re-ranking stage based on spatial verification is still required to boost performance. To improve the effectiveness of such instance retrieval systems while avoiding the computational complexity of a re-ranking stage, we explored the geometric correlations among local features and incorporate these correlations with each individual match to form a transformation consistency in rotation and scale space. This weak geometric correlation consistency can be used to effectively eliminate inconsistent feature matches and can be applied to all candidate images at a low computational cost. Experimental results on three standard evaluation benchmarks show that the proposed approach results in a substantial performance improvement compared with recently proposed methods

Improving instance search performance in video collections

This thesis presents methods to improve instance search and enhance user performance while browsing unstructured video collections. Through the use of computer vision and information retrieval techniques, we propose novel solutions to analyse visual content and build a search algorithm to address the challenges of visual instance search, while considering the constraints for practical applications. Firstly, we investigate methods to improve the effectiveness of instance search systems for finding object instances which occurred in unstructured video content. Using the bag of feature framework, we propose a novel algorithm to use the geometric correlation information between local features to improve the accuracy of local feature matching, thus improve the performance of instance search systems without introducing much computation cost. Secondly, we consider the scenario that the performance of instance search systems may drop due to the volume of visual content in large video collections. We introduce a search algorithm based on embedded coding to increase the effectiveness and efficiency of instance search systems. And we participate in the international video evaluation campaign, TREC Video Retrieval Evaluation, to comparatively evaluate the performance of our proposed methods. Finally, the exploration and navigation of visual content when browsing large unstructured video collections is considered. We propose methods to address such challenges and build an interactive video browsing tool to improve user performance while seeking interesting content over video collections. We construct a structured content representation with similarity graph using our proposed instance search technologies. Considering the constraints related to real world usability, we present a flexible interface based on faceted navigation to enhance user performance when completing video browsing tasks. This thesis shows that user performance can be enhanced by improving the effectiveness of instance search approaches, when seeking information in unstructured video collection. While covering many different aspects of improving instance search in this work, we outline three potential directions for future work: advanced feature representation, data driven rank and cloud-based search algorithms