Change detection in marine observatory image streams using Bi-Domain Feature Clustering

Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species is proposed. The change detection method uses the new Bi-Domain Feature Clustering (BDFC). BDFC integrates the location of a feature vector in the feature space as well as the location in the image into the clustering. Firstly, BDFC is applied to a time dependent representation of the image stream to identify regions of similar change. Secondly it is applied to a time independent representation to group these changes into categories. These categories can rapidly be assessed by a human observer to bypass the time consuming inspection of the whole data set. To make the posterior browsing of detected changes more efficient, a relevance factor computed for each category is proposed. The approach is demonstrated with experimental runs, using images from the Lofoten Vesteralen ocean observatory, showing the potential to harvest changes of interest and novelties in large image collections.