SEMI-SUPERVISED BIASED MAXIMUM MARGIN ANALYSIS FOR INTERACTIVE IMAGE RETRIEVAL

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. 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. To explore solutions to overcome these two drawbacks, in this paper, we propose a biased maximum margin analysis and a semi-supervised BMMA for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. During the past few years, content-based image retrieval (CBIR) has gained much attention for its potential applications in multimedia management. It is motivated the explosive growth of image records and the online accessibility of remotely stored images. An effective search scheme is urgently required to manage the huge image database. Different from the traditional search engine, in CBIR, an image query is described by using one or more example images, and low-level visual features (e.g., color , texture, shape, etc.) are automatically extracted to represent the images in the database. However, the low-level features captured from the images may not accurately characterize the high-level semantic concepts. To narrow down the so-called semantic gap, relevance feedback (RF) was introduced as a powerful tool to enhance the performance of CBIR, the query movement and the re-weighting techniques. A selforganizing map was used to construct the RF algorithms. In [16], one-class support vector machine (SVM) estimated the density of positive feedback samples. Derived from one-class SVM, a biased SVM inherited the merits of one-class SVM but incorporated the negative feedback samples. Considering the geometry structure of image low-level visual features, and proposed manifoldlearning-based approaches to find the intrinsic structure of images and improve the retrieval performance. With the observation that "all positive examples are alike; each negative example is negative in its own way," RF was formulated as a biased subspace learning problem, in which there is an unknown number of classes, but the user is only concerned about the positive class. However, all of these methods have some limitations. Heuristically based, the density estimation method in ignores any information contained in the negative feedback samples, and the discriminant subspace learning techniques in often suffer from the so- called -small sample size" problem. Regarding the positive and negative feedbacks as two different groups, classification-based RFs have become a popular technique in the CBIR community. However, RF is very different from the traditional classification problem because the feedbacks provided by the user are often limited in real-world image retrieval systems. Therefore, small sample learning methods are most promising for RF. Random sampling techniques were applied to alleviate unstable, biased, and over fitting problems in SVM RF proposed a multi training SVM method by adapting a constraining technique and a random sampling method. Nevertheless, most of the SVM RF approaches ignore the basic difference between the two distinct groups of feedbacks, i.e., all positive feedbacks share a similar concept while each negative feedback usually varies with different concepts. For instance, a typical set of feedback samples in RF iteration is shown in Fig. 1. All the samples labeled as positive feedbacks share a common concept (i.e., elephant), while each sample labeled as negative feed back varies with diverse concepts (i.e., flower, horse, banquet, hill, etc.). Traditional to explore solutions to these two aforementioned problems in the current exiiscdogy, we propose a biased maximum margin analysis (BMMA) and a SVMRF techniques treat positive and negative feedbacks equally. Directly using SVM as an RF scheme is potentially damaging to the performance of CBIR systems. One problem stems from the fact that different semantic concepts live in different subspaces and each image can live in many different subspaces, and it is the goal of RF schemes to figure out "which one". However, it will be a burden for traditional SVM-based RF schemes to tune the internal parameters to adapt to the changes of the subspace. Such difficulties have severely degraded the effectiveness of traditional SVM RF approaches for CBIR. Additionally, it is problematic to incorporate the information of unlabeled samples into traditional SVM