Low-level features tend to achieve unsatisfactory retrieval results in remote sensing image retrieval community because of the existence of semantic gap. In order to improve retrieval precision, visual attention model is used to extract salient objects from image according to their saliency. Then color and texture features are extracted from salient objects and regarded as feature vectors for i...

”Semantic gap” is an open challenging problem in content-based image retrieval. It reflects the discrepancy between low-level imagery features used by the retrieval algorithm and high-level concepts required by system users. This paper introduces a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), to tackle the semantic gap problem. CLUE is built o...

One important challenge in modern Content-Based Medical Image Retrieval (CBMIR) approaches is represented by the semantic gap, related to the complexity of the medical knowledge. Among the methods that are able to close this gap in CBMIR, the use of medical thesauri/ontologies has interesting perspectives due to the possibility of accessing on-line updated relevant webservices and to extract re...

This paper attempts to provide a comprehensive review and characterize the problem of the semantic gap that is the key problem of content-based image retrieval and the current attempts in high-level semantic-based image retrieval being made to bridge it. Major recent publications are included in this review covering different aspects of the research in the area of high-level semantic features. ...

Compared with content-based image retrieval, annotationbased image retrieval is more practical in some application domains. Users’ information needs and the semantic contents of images can be represented by textual information more easily. We describe two problems which are unique to annotation-based image retrieval and would be worthy of further research. Contextual information embedded in dat...

Large amounts of histology images are captured and archived in pathology departments due to the ever expanding use of digital microscopy. The ability to manage and access these collections of digital images is regarded as a key component of next generation medical imaging systems. This paper addresses the problem of retrieving histopathology images from a large collection using an example image...

We study several semantic concept-based query expansion and re-ranking scheme and compare different ontology-based expansion methods in image search and retrieval. In particular, we exploit the two concept similarities of different concept expansion ontologyWordNet Similarity, Wikipedia Similarity. Furthermore, we compare the keywords semantic distance with the precision of image search results...

one of the challenging issues in managing the existing large digital image libraries and databases is content based image retrieval (cbir). the accuracy of image retrieval methods in cbir is subject to effective extraction of image features such as color, texture, and shape. in this paper, we propose a new image retrieval method using contourlet transform coefficients to index texture of the im...

In this paper, we present an approach for the retrieval of natural scenes based on a semantic modeling step. Semantic modeling stands for the classification of local image regions into semantic classes such as grass, rocks or foliage and the subsequent summary of this information in so-called conceptoccurrence vectors. Using this semantic representation, images from the scene categories coasts,...

Content based image retrieval is an active area of medical imaging research. One use of content based image retrieval (CBIR) is presentation of known, reference images similar to an unknown case. These comparison images may reduce the radiologist’s uncertainty in interpreting that case. It is, therefore, important to present radiologists with systems whose computed-similarity results correspond...