Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Change Detection in Multi-temporal Images Using Multistage Clustering for Disaster Recovery Planning

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering\u27s based on Euclidian distance

Investigation to develop a multistage forest sampling inventory system using ERTS-1 imagery

The author has identified the following significant results. The annotation system produced a RMSE of about 200 m ground distance in the MSS data system with the control data used. All the analytical MSS interpretation models tried were highly significant. However, the gains in forest sampling efficiency that can be achieved by using the models vary from zero to over 50 percent depending on the area to which they are applied and the sampling method used. Among the sampling methods tried, regression sampling yielded substantial and the most consistent gains. The single most significant variable in the interpretation model was the difference between bands 5 and 7. The contrast variable, computed by the Hadamard transform was significant but did not contribute much to the interpretation model. Forest areas containing very large timber volumes because of large tree sizes were not separable from areas of similar crown cover but containing smaller trees using ERTS image interpretation only. All correlations between space derived timber volume predictions and estimates obtained from aerial and ground sampling were relatively low but significant and stable. There was a much stronger relationship between variables derived from MSS and U2 data than between U2 and ground data

CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance