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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/. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; H.4.0 [Information Systems Applications]: General; 1.2.6 [Artificial Intelligence] General Terms: Algorithms, Design, Experimentation, Performance Additional Key Words and Phrases: Content-based image retrieval, support vector machines, active learning, semisupervised learning, batch mode active learning, human-computer interaction ACM Reference Format: Hoi, S. C. H., Jin, R., Zhu, J., and Lyu, M. R. 2009. Semisupervised SVM batch mode active learning with applications to image retrieval. ACM Trans. Inform. Syst. 27, 3, Article 16 (May 2009), 29 pages. DOI = 10.1145/1508850.1508854 http://doi.acm.org/10.1145/1508850.1508854

Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual form, which is then solved by some quadratic program solver consequently. In this correspondence, we propose a new formulation for robust regularized kernel regression under the theoretical framework of regularization networks and then tackle the optimization problem directly in the primal. We show that the primal and dual approaches are equivalent to achieving similar regression performance, but the primal formulation is more efficient and easier to be implemented than the dual one. Different from previous work, our approach also optimizes the bias term. In addition, we show that the proposed solution can be easily extended to other noise-reliable loss function, including the Huber-[member of] insensitive loss function. Finally, we conduct a set of experiments on both artificial and real data sets, in which promising results show that the proposed method is effective and more efficient than traditional approaches. Index Terms--Kernel regression, regularized least squares (RLS), robust estimator, support vector machine (SVM).

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the Tog of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named Soft Label Support Vector Machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically. Index Terms--Content-based image retrieval, relevance feedback, log-based relevance feedback, tog data, user issues, semantic gap, support vector machines.