19-01-2013, 10:42 AM
Semi supervised Biased Maximum Margin Analysis for Interactive Image Retrieval
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Objective of the Project:
With many potential practical applications, content- based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine
(SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not take into account the unlabeled samples, although they are very helpful in constructing a good classifier.
Methodology:
A Semi-Supervised Hashing (SSH) technique that can leverage semantic similarity using labeled data while remaining robust to overfitting. SSH is also much faster than existing supervised hashing methods and can be easily scaled to large datasets. The SSH problem is cast as a data-dependent projection learning problem. We provide a rigorous formulation in which a supervised term tries to minimize the empirical error on the labeled data while an unsupervised term provides effective regularization by maximizing desirable properties like variance and independence of individual bits. We show that the resulting formulation can be easily relaxed and solved as a standard eigenvalue problem. Furthermore, by relaxing the orthogonality constraints, one can get even better hash codes at no added computational cost. Table 1 summarizes a taxonomy of popular methods and places our method in context.
EXPECTED OUTCOME OF THE PROJECT:
In this paper, we propose a biased maximum margin analysis (BMMA) and a semisupervised BMMA (SemiBMMA) for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. The BMMA differentiates positive feedbacks from negative ones based on local analysis, whereas the SemiBMMA can effectively integrate information of unlabeled samples by introducing a Laplacian regularize to the BMMA. We formally formulate this problem into a general subspace learning task and then propose an automatic approach of determining the dimensionality of the embedded subspace for RF. Extensive experiments on a large real-world image database demonstrate that the proposed scheme combined with the SVM RF can significantly improve the performance of CBIR systems.
Positive module
With the observation that “all positive examples are alike; each negative example is negative in its own way,” the two groups of feedbacks have distinct properties for CBIR. However, the traditional SVM RF treats the positive and negative feedbacks equally. To alleviate the performance degradation when using SVM as an RF scheme for CBIR, we explore solutions based on the argument that different semantic concepts lie in different subspaces and each image can lie in many different concept subspaces .We formally formulate this problem into a general subspace learning problem and propose a BMMA for the SVM RF scheme..