In order to improve the efficiency of massive medical images retrieval, against the defects of the single-node medical image retrieval system, a massive medical images retrieval system based on Hadoop is put forward. Brushlet transform and Local binary patterns algorithm are introduced firstly to extract characteristics of the medical example image, and store the image feature library in the HD...

Content-based image retrieval systems were introduced as an alternative to avoid the need of manual tagging in traditional keyword-based image retrieval systems. However, the representation of image using visual features only involves a loss of information which is referred to as semantic gap. A number of techniques have been proposed to deal with ‘semantic gap’. This paper reviews existing app...

We propose a novel image retrieval framework centered around classi cation-driven search for a weighted similarity metric for image retrieval. This approach is rmly rooted in Bayes decision theory. Given a well-de ned image set, we argue that image classi cation and image retrieval share fundamentally the same goal. Thus, the distance metric de ning a classi er that performs well on the image s...

Large amounts of spatial data are becoming available today due to the rapid development of remote sensing techniques. Several retrieval systems are proposed to retrieve necessary, interested and effective information such as keyword based image retrieval and content based image retrieval. However, the results of these approaches are generally unsatisfactory, unpredictable and don’t match human ...

As a scene graph compactly summarizes the high-level content of an image in structured and symbolic manner, similarity between graphs two images reflects relevance their contents. Based on this idea, we propose novel approach for image-to-image retrieval using measured by neural networks. In our approach, networks are trained to predict proxy measure, computed from human-annotated captions pre-...