A Framework for Interactive Content-Based Image Retrieval

With the exponential increase in the size of digital image databases in the past few years, traditional way of manually annotating the images with text and then using text-based queries for image retrieval has been giving way to content-based image retrieval (CBIR) systems that use the visual contents of the images to automatically index and retrieve digital images. However, there is always a gap between high-level human perception of an image and the low-level image features used to describe its contents. This gap between low-level image features and semantic image content is the major bottleneck faced by traditional CBIR systems. Modern CBIR systems overcome this problem by using interactive learning, bringing the user in the loop. Such systems learn from feedbacks given by the user about the relevance or irrelevance of the current retrieval results. This paper presents a framework for interactive content-based image retrieval. Considering relevance feedback as a learning problem, a learning machine based on radial basis functions (RBF) neural networks (NN) is implemented and the system has been tested for its effectiveness using a database of 10,000 images