As digitisation era will come, a large number of multimedia datas (image, video, etc.) are stored in the database by digitisation, and its retrieval system is more and more important. Video is huge in frames amount, in order to search effectively and fast, the first step will detect and examine the place where the scene changes in the video, cut apart the scene, find out the key frame from each scene, regard as analysis that the index file searches with the key frame. The scene changes the way and divides into the abrupt and the gradual transition. But in the video, even if in the same scene, incident of often violent movements or the camera are moving etc. happens, and obscure with the gradual transition to some extent. Thus this papper gets the main component from every frame in the video using principal component analysis (PCA), reduce the noise to interfere, and classify these feauture points with support vector clustering, it is the same class that the close feature points is belonged to. If the feature points are located between two groups of different datas, represent the scene is changing slowly in the video, detect scene change by this.

The need to quantify distance between two groups of objects is prevalent throughout the signal processing world. The difference of group means computed using the Euclidean, or L2 distance, is one of the predominant distance measures used to compare feature vectors and groups of vectors, but many problems arise with it when high data dimensionality is present. Maximum mean discrepancy (MMD) is a recent unsupervised kernel-based pattern recognition method which may improve differentiation between two distinct populations over many commonly used methods such as the difference of means, when paired with the proper feature representations and kernels. MMD-based distance computation combines many powerful concepts from the machine learning literature, such as data distribution-leveraging similarity measures and kernel methods for machine learning. Due to this heritage, we posit that dissimilarity-based classification and changepoint detection using MMD can lead to enhanced separation between different populations. To test this hypothesis, we conduct studies comparing MMD and the difference of means in two subareas of image analysis and understanding: first, to detect scene changes in video in an unsupervised manner, and secondly, in the biomedical imaging field, using clinical ultrasound to assess tumor response to treatment. We leverage effective computer vision data descriptors, such as the bag-of-visual-words and sparse combinations of SIFT descriptors, and choose from an assessment of several similarity kernels (e.g. Histogram Intersection, Radial Basis Function) in order to engineer useful systems using MMD. Promising improvements over the difference of means, measured primarily using precision/recall for scene change detection, and k-nearest neighbour classification accuracy for tumor response assessment, are obtained in both applications.