1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Semisupervised SVMbatch mode activelearning withapplications to imageretrieval Steven C. H. HoiRong JinJianke Zhu2009 год

Semisupervised SVM batch mode active learning with applications to image retrieval
статья из журнала

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: Support vector machine (SVM) active learning is one popular and successful technique for relevance feedback in content-based image retrieval (CBIR). Despite the success, conventional SVM active learning has two main drawbacks. First, the performance of SVM is usually limited by the number of labeled examples. It often suffers a poor performance for the small-sized labeled examples, which is the case in relevance feedback. Second, conventional approaches do not take into account the redundancy among examples, and could select multiple examples that are similar (or even identical). In this work, we propose a novel scheme for explicitly addressing the drawbacks. It first learns a kernel function from a mixture of labeled and unlabeled data, and therefore alleviates the problem of small-sized training data. The kernel will then be used for a batch mode active learning method to identify the most informative and diverse examples via a min-max framework. Two novel algorithms are proposed to solve the related combinatorial optimization: the first approach approximates the problem into a quadratic program, and the second solves the combinatorial optimization approximately by a greedy algorithm that exploits the merits of submodular functions. Extensive experiments with image retrieval using both natural photo images and medical images show that the proposed algorithms are significantly more effective than the state-of-the-art approaches. A demo is available at http://msm.cais.ntu.edu.sg/LSCBIR/.
Год издания: 2009
Авторы: Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Издательство: Association for Computing Machinery
Источник: ACM transactions on office information systems
Ключевые слова: Image Retrieval and Classification Techniques, Advanced Image and Video Retrieval Techniques, Machine Learning and Algorithms
Другие ссылки: ACM transactions on office information systems (HTML)
Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (HTML)
CiteSeer X (The Pennsylvania State University) (PDF)
CiteSeer X (The Pennsylvania State University) (HTML)