Nagul Cooharojananone

Home video summarization by shot characteristics and user's feedback

In this paper, we propose a home video summarization by representing a group of Representative frames (R-frames). The number of R-frames depends on shot characteristics which is shot duration and shot motion activity. For each shot, we apply an adaptive sub-sampling algorithm to extract the R-frames that contain only the high frame difference. When events of shots are not related to each other (One tape contains many events), it is desired to retrieve more information from more shots. Our algorithm allows users to select the number of shots appearing in the summarized video which give an optional way to understand the original sequence. In our experiments, we summarize video into variable number of shot appearing in the summary and evaluate the summary by userss subjective evaluations. We also summarize the home video by users feedback. The user is asked to select the extracted R-frames as a training data. We apply the Support Vector Machine algorithm (SVM) to train and classify. The result from users feedback shows that SVM retrieves the frames according to the users desired.

New similarity measure for color image indexing

In this paper, we propose a new similarity measure for color images, that is angular distance of cumulative histogram. The measure is compared to previous popular measure that is cumulative L1 distance measure on RGB and HSV color space. We show that our method produces a better result than cumulative L1 distance measure. Moreover, to increase the accuracy, we introduce the weighting method. Weights are applied to the RGB similarity, DR DG, DB, according to a Hue histogram of the query image. The weighting method increase accuracy and perceptually relevant result.