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Multi-Label Classification With Its Application Into Multimedia Analysis

Posted on:2010-10-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:G J QiFull Text:PDF
GTID:1118360275455457Subject:Pattern Recognition and Intelligent Systems
Abstract/Summary:PDF Full Text Request
Automatically annotating concepts for multimedia is a key to semantic-level video browsing,search and navigation.The research on this topic evolved through two paradigms.The first paradigm used binary classification to detect each individual concept in a concept set.It achieved only limited success,as it did not model the inherent correlation between concepts,e.g.,urban and building.The second paradigm added a second step on top of the individual-concept detectors to fuse multiple concepts.However, its performance varies because the errors incurred in the first detection step can propagate to the second fusion step and therefore degrade the overall performance. To address the above issues,we first propose a third paradigm which simultaneously classifies concepts and models correlations between them in a single step by using a novel Correlative Multi-Label(CML) framework.We compare the performance between the proposed approach and the state-of-the-art approaches in the first and second paradigms on the widely used TRECVID data set.We report superior performance from the proposed approach.On the other hand,conventional active learning dynamically constructs the training set only along the sample dimension.While this is the right strategy in binary classification,it is sub-optimal for multi-label image classification.We argue that for each selected sample,only some effective labels need to be annotated while others can be inferred by exploring the label correlations.The reason is the contributions of different labels to minimizing the classification error are different due to the inherent label correlations.To this end,we propose to select sample-label pairs,rather than only samples,to minimize a multi-label Bayesian classification error bound.We call it two-dimensional active learning because it considers both the sample dimension and the label dimension.Furthermore because the number of training samples is increasing rapidly over time due to active learning,it becomes intractable for the offline learner to retrain a new model on the whole training set.So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance un- der a set of multi-label constraints.The effectiveness and efficiency of the proposed method are evaluated on two benchmark datasets and a realistic image collection from a real-world image sharing website - Corbis.
Keywords/Search Tags:Multi-Label Pattern Classification, Multimedia Analysis
PDF Full Text Request
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