Relevance Feedback In Content-based Image Retrieval Research Articles

Robust discriminative extreme learning machine for relevance feedback in image retrieval

Relevance feedback (RF) has long been an important approach for multi-media retrieval because of the semantic gap in image content, where SVM based methods are widely applied to RF of content-based image retrieval. However, RF based on SVM still has some limitations: (1) the high dimension of image features always make the RF time-consuming; (2) the model of SVM is not discriminative, because labels of image features are not sufficiently exploited. To solve above problems, we proposed robust discriminative extreme learning machine (RDELM) in this paper. RDELM involved both robust within-class and between-class scatter matrices to enhance the discrimination capacity of ELM for RF. Furthermore, an angle criterion dimensionality reduction method is utilized to extract the discriminative information for RDELM. Experimental results on four benchmark datasets (Corel-1K, Corel-5K, Corel-10K and MSRC) illustrate that our proposed RF method in this paper achieves better performance than several state-of-the-art methods.

A new SVM-based active feedback scheme for image retrieval

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). Support vector machine (SVM) active learning is one popular and successful technique for relevance feedback in CBIR. Despite the success, for conventional SVM active learning, the users are usually not so patience to label a large number of training instances in the relevance feedback round. To overcome this limitation, a new SVM-based active feedback using ensemble multiple classifiers is proposed in this paper. Firstly, we select the most informative images by using active learning method for user to label, and quickly learn a boundary that separates the images that satisfy the user׳s query concept from the rest of the dataset. Then, a set of moderate accurate one-class SVM classifiers are trained separately by using different sub-features vectors. Finally, we compute the weight vector of component SVM classifiers dynamically by using the parameters for positive and negative samples, and combine the results of the component classifiers to form an output code as a hypothesized solution to the overall image retrieval problem. Experiments on large databases show that the proposed algorithms are significantly more effective than the state-of-the-art approaches.

Medical Image Retrieval Based on Semi-Supervised Learning

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.