A method for accurate scene segmentation using two kinds of directed graph obtained by object matching and audio features is proposed. Generally, in audiovisual materials, such as broadcast programs and movies, there are repeated appearances of similar shots that include frames of the same background, object or place, and such shots are included in a single scene. Many scene segmentation methods based on this idea have been proposed; however, since they use color information as visual features, they cannot provide accurate scene segmentation results if the color features change in different shots for which frames include the same object due to camera operations such as zooming and panning. In order to solve this problem, scene segmentation by the proposed method is realized by using two novel approaches. In the first approach, object matching is performed between two frames that are each included in different shots. By using these matching results, repeated appearances of shots for which frames include the same object can be successfully found and represented as a directed graph. The proposed method also generates another directed graph that represents the repeated appearances of shots with similar audio features in the second approach. By combined use of these two directed graphs, degradation of scene segmentation accuracy, which results from using only one kind of graph, can be avoided in the proposed method and thereby accurate scene segmentation can be realized. Experimental results performed by applying the proposed method to actual broadcast programs are shown to verify the effectiveness of the proposed method.

We present a robust object-based watermarking algorithm using the scale-invariant feature transform (SIFT) in conjunction with a data embedding method based on Discrete Cosine Transform (DCT). The message is embedded in the DCT domain of randomly generated blocks in the selected object region. To recognize the object region after being distorted, its SIFT features are registered in advance. In the detection scheme, we extract SIFT features from the distorted image and match them with the registered ones. Then we recover the distorted object region based on the transformation parameters obtained from the matching result using SIFT, and the watermarked message can be detected. Experimental results demonstrated that our proposed algorithm is very robust to distortions such as JPEG compression, scaling, rotation, shearing, aspect ratio change, and image filtering.