A Study on the Visual Information Search and Ranking Refinement
Date Issued
2010
Date
2010
Author(s)
Hsiao, Jen-Hao
Abstract
The development of technology such as digital cameras and mobile telephones equipped with digital imaging sensors has generated a huge amount of multimedia data such as images and videos. With the world-wide spread of the Internet, the amount of easily accessible visual information that an ordinary people can reach has become so vast. The topic of efficacious access and retrieval of visual information has thus become a very active research topic in multimedia community.
The main goal of this dissertation is to enable visual search of images in a large image collection. Two different types of visual information search, near-duplicate image detection and image object retrieval, are explored for different application fields. In addition to the fundamental search issues, we also study the problem of ranking refinement, whose goal is to improve an existing ranking function by a set of labeled or pseudo-relevant instances. We are, particularly, interested in learning a better query model using two complementary sources of information: the information from the base ranker (i.e., the existing ranking function) and the information from users’ feedbacks.
In this dissertation, we first present a new framework called the extended feature set (EFS) for detecting copies of images. Instead of dealing directly with the feature selection problem, which is hard to solve and domain dependent, the proposed EFS framework addresses the copy detection problem by using prior simulated attacks. This technique enhances the detection accuracy by generating features with the necessary invariance to resist various types of image manipulation. Furthermore, the proposed approach can be integrated into existing copy detectors to further improve their performance.
We then present a novel language-model-based approach with pseudo-relevant feedback to address the vocabulary problem in the visual bag-of-words-based (VBOW-based) search, which is one leading method for image object retrieval. We employ the pseudo positive images produced in response to the original query as a set of “cues” to gradually refine the query language model. Unlike traditional approaches that only ruggedly append feedback information into the original query, the proposed approach reconstructs the query language model with finer granularities so that the query concepts can be captured more accurately.
Finally, we describe the Intention-Focused Active Reranking, an approach for automatically finding the right information from user’s labeled data to re-estimate the query model under the active feedback framework. Three novel strategies are proposed to boost the performance of the base ranker (i.e., a given ranking function): (1) an active selection criterion, which obtains a small number of feedback images that are the most informative to the base ranker for user labeling; (2) the user intention verification, which captures the user’s intention in object level to alleviate the query drift problem; (3) a discriminative query model re-estimation, which augments the generative approach with a model of the discriminative information conveyed by positive and negative feedback information.
The proposed approaches are experimentally evaluated using real world image data sets. Experiment results demonstrate that the proposed EFS approach can substantially enhance the accuracy of copy detection, and the proposed ranking refinement algorithms can bring significant improvement in the image object retrieval accuracy over a non-feedback baseline, and achieve better performance than conventional feedback approaches.
Subjects
Copy detection
image object retrieval
relevance feedback
Type
thesis
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