Interactive visualization of video content and associated description for semantic annotation

In this paper, we present an intuitive graphic fra- mework introduced for the effective visualization of video content and associated audio-visual description, with the aim to facilitate a quick understanding and annotation of the semantic content of a video sequence. The basic idea consists in the visualization of a 2D feature space in which the shots of the considered video sequence are located. Moreover, the temporal position and the specific content of each shot can be displayed and analysed in more detail. The selected fea- tures are decided by the user, and can be updated during the navigation session. In the main window, shots of the consi- dered video sequence are displayed in a Cartesian plane, and the proposed environment offers various functionalities for automatically and semi-automatically finding and annotating the shot clusters in such feature space. With this tool the user can therefore explore graphically how the basic segments of a video sequence are distributed in the feature space, and can recognize and annotate the significant clusters and their structure. The experimental results show that browsing and annotating documents with the aid of the proposed visuali- zation paradigms is easy and quick, since the user has a fast and intuitive access to the audio-video content, even if he or she has not seen the document yet

Spatial Visualization For Content-Based Image Retrieval

In traditional content-based image retrieval (CBIR), the retrieved images are displayed in order of decreasing similarities from the query and can be considered as a 1-D display. In this paper a novel optimized technique is proposed to visualize the retrieved images not only in order of their decreasing similarities but also according to their mutual similarities visualized on a 2-D screen. Principle Component Analysis (PCA) is first performed on the retrieved images to project the images from the original high dimensional feature space to 2-D screen. The result of PCA analysis is denoted as a PCA Splat. To minimize the overlap between images, a constrained nonlinear optimization approach is used. The experimental results show a more perceptually intuitive and informative visualization of the retrieval results. The proposed technique not only provides a better understanding of the query results but also aids the user in forming a new query

Spatial Visualization for Content-Based Image Retrieval

In traditional content-based image retrieval (CBIR), the retrieved images are displayed in order of decreasing similarities from the query and can be considered as a 1-D display. In this paper, a novel optimized technique is proposed to visualize the retrieved images not only in order of their decreasing similarities but also according to their mutual similarities visualized on a 2-D screen. Principle Component Analysis (PCA) is first performed on the retrieved images to project the images from the original high dimensional feature space to 2-D screen. The result of PCA analysis is denoted as a PCA Splat. To minimize the overlap between images, a constrained nonlinear optimization approach is used. The experimental results show a more perceptually intuitive and informative visualization of the retrieval results. The proposed technique not only provides a better understanding of the query results but also aids the user in forming a new query