Image retrieval by using histogram equalization and CBIR

In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation. General content-based image retrieval (CBIR) also could be improved by the proposed approach in a similar manner as text-based retrieval is improved. In this case no text information is available, but only visual features are used. The CBIR identifies relevant articles as text-based retrieval does in the multimodal method. Annotations and ROIs in retrieved images can be identified by the annotation recognizer and then be used to re-rank the results. At present, images needed for instructional purposes or clinical decision support (CDS) appear in specialized databases or in biomedical publications and are not meaningfully retrievable using primarily text-based retrieval systems. Our goal is to automatically annotate images extracted from scientific publications with respect to their usefulness for CDS. A future clinical decision support system (CDSS) could then provide images relevant to a clinical query or to queries for special cases important in educational settings. An important step toward attaining the goal is automatically annotating images and related text.