MIRACLE's Combination of Visual and Textual Queries for Medical Image Retrieval

For the modality classification and image retrieval tasks, our best results were obtained using mixed approaches, indicating the importance of both textual and visual features for

queries cbir without case backoff (mixed): Essie search like run (3) but form- ing the query using the captions of the top 3 images retrieved using a content- based

To generate the feature vectors at different levels of abstraction, we extract both visual concept- based feature based on a “bag of concepts” model comprising color and texture

• Content Based Image Retrieval (CBIR) systems use low-level image features to return images similar to an image used as example.. The main problem of this approach is that

Our system consists of three different modules: the textual (text-based) retrieval module, which indexes the case descriptions to look for those descriptions which are more

For the medical image retrieval task, we distinguished gray images from color images and used different kinds of visual features to describe them. Our submission ranked second among

Method A is identical to the approach we have chosen for the medical retrieval task, except that here no textual information was available, and that we used appearance-based

For each image, the similarity scores of three runs were normalized and linear combined using weights 0.7, 0.2, and 0.1 for textual query, example image, and generated visual