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In order to mimic the representation of textual documents, some approaches have recently been proposed to represent visual contents in terms of visual words in many applications such as object recognition and image annotation. In this paper, we propose to build an effective visual vocabulary by using Hierarchical Gaussian Mixture model instead of traditional clustering methods. In addition, Probabilistic...
Recently, keypoint descriptors such as Scale Invariant Feature Transform (SIFT) have been proved promising in similarity retrieval of images, which adopts matching score as similarity. However, the matching score is easy to be decreased once there are little variances between image details, and hence lead to low retrieval performance. In this paper, we propose a novel retrieval approach that improves...
Aimed at the application requirements of content-based image retrieval technology on the Internet, firstly, some key techniques and application ways are researched, then the principles and methods how to reduce the ??gap?? between low-level visual features and high-level semantic description of image are analyzed for improving the efficiency and precision of image retrieval. At last, taken several...
We address the issue of categorizing scenes from feature films into semantic classifications based on the audio-visual cues. Specifically, we first exploit the grammar of film production to specify the semantic content of scenes. Then, each scene is classified into one of the following categories: conversation, action and suspense. Finally, to achieve more specific scene and consist with human perception,...
Automatic Image Annotation (AIA) tries to minimize the manual effort for image annotation. However, the performance of the AIA approaches is not satisfactory. The interaction of user is needed to solve this problem. The annotation is refined during the interaction by using semantic-based relevance feedback. This approach has a limit as only the annotations of found images during the interaction are...
Classifying natural scenes into semantic categories has always been a challenging task. So far, many works in this field are primarily intended for single label classification, where each scene example is represented as a single instance vector. The multi-instance multi-label (MIML) learning framework proposed by Z.H. Zhou et al. provides a new solution to the problem of scene classification in a...
Video applications usually involve a large number of moving objects. Moving objects refer to semantic realworld entities denoting a coherent spatial region and being automatically computed by the continuity of spatial and temporal low-level features, such as color and motion. In surveillance application, spatial and temporal relationships among these objects should be efficiently supported and retrieved...
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