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 image retrieval. Experimental results demonstrate that our method improves retrieval results and obtains higher precision
A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from tr...
Remote sensing (RS) scene classification plays an important role in a wide range of RS applications....
In image retrieval, global features related to color or texture are commonly used to describe the im...
Abstract. In this paper we propose to employ human visual attention models for content based image r...
Based on the bottom-up human visual attention model, a procedure for feature description and image r...
In recent years, numerous remote sensing platforms for Earth observation have been developed and tog...
In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distr...
Although there has been successful work in developing image mining algorithms to extract in-formatio...
High resolution remote sensed image data continues to become more accessible. One consequence of thi...
Recent research on computational modeling of visual attention has demonstrated that a bottom-up appr...
Remote sensing (RS) image retrieval is of great significant for geological information mining. Over ...
In this paper, we present an image retrieval technique for specific objects based on salient regions...
Searching for an image in a database is important in different applications; hence, many algorithms ...
Saliency algorithms in content-based image retrieval are employed to retrieve the most important reg...
Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-...
A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from tr...
Remote sensing (RS) scene classification plays an important role in a wide range of RS applications....
In image retrieval, global features related to color or texture are commonly used to describe the im...
Abstract. In this paper we propose to employ human visual attention models for content based image r...
Based on the bottom-up human visual attention model, a procedure for feature description and image r...
In recent years, numerous remote sensing platforms for Earth observation have been developed and tog...
In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distr...
Although there has been successful work in developing image mining algorithms to extract in-formatio...
High resolution remote sensed image data continues to become more accessible. One consequence of thi...
Recent research on computational modeling of visual attention has demonstrated that a bottom-up appr...
Remote sensing (RS) image retrieval is of great significant for geological information mining. Over ...
In this paper, we present an image retrieval technique for specific objects based on salient regions...
Searching for an image in a database is important in different applications; hence, many algorithms ...
Saliency algorithms in content-based image retrieval are employed to retrieve the most important reg...
Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-...
A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from tr...
Remote sensing (RS) scene classification plays an important role in a wide range of RS applications....
In image retrieval, global features related to color or texture are commonly used to describe the im...