visual-based image retrievalbased on the visual similarity over the entire image is very useful when targeting various kinds of large-volume content. This method generally divides an image into grid-shaped blocks and...
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ISBN:
(纸本)9781479921904
visual-based image retrievalbased on the visual similarity over the entire image is very useful when targeting various kinds of large-volume content. This method generally divides an image into grid-shaped blocks and uses similarities based on a comparison of image features between corresponding block regions in two different images. However, the method sometimes fails in terms of object-conscious retrieval when their backgrounds are almost the same but the only object is different or object's positions and/or sizes are different. In this paper, we propose a new method featuring the reallocation of some blocks into the object region (OB-blocks) and the new similarity score with placing weight on the OB-blocks, which are derived from visual saliency map. Our proposed method could realize the "visual-based and object-conscious" imageretrieval. We verified the effectiveness of this method through comparison experiments.
With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiv...
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The technology of medical data production has been rapidly changed over the past few years. Modern computer technology has created the possibility of creating multi-modal medical images. Medical data often contain mul...
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The technology of medical data production has been rapidly changed over the past few years. Modern computer technology has created the possibility of creating multi-modal medical images. Medical data often contain multi-modal information such as visual information (image) as well as textual information. Both types of information are important for medical retrieval system (MRS). Due to the information limitation at different levels of sources, the application of information fusion becomes a real need in medical application. In this research, an information fusion framework was built to develop the multi-modality medical imageretrieval system (IFM3IRS). The framework utilizes two sources of information involving text and visual-basedretrieval process. The application is based on sequential order where the result from text-based process will automatically be the input in visual-based process. The main contributions of this paper are the development of a new ranking model called MedHieCon ranking model which applies semantic concepts of modality, anatomy and pathology in text-based process and also the learning approach of medical images using medical concept model in visual-based process. imageCLEFmed 2010 data collection was used to evaluate IFM3IRS and it shows that our information fusion framework is in top list among other researchers. Although text-basedretrieval system has proven to be a better performance in MRS;it is significant to determine the overall performance improvements which include the fusion of text and image.
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