Machine learning (ML) algorithms have shown great potential in edge-computing environments, however, the literature mainly focuses on model inference only. We investigate how ML can be operationalized and how in-situ curation can improve the quality of edge applications, in the context of ML-assisted environmental surveys. We show that camera-enabled ML systems deployed on edge devices can enable scientists to perform real-time monitoring of species of interest or characterization of natural habitats. However, the benefit of this new technology is only as good as the quality and accuracy of the edge ML model inferences. In this demonstration, we show that with small additional time investment, domain scientists can manually curate ML model outputs and thus obtain highly reliable scientific insights, leading to more effective and scalable environmental surveys.

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.

In this paper we present RetrievalLab, a content based retrieval tool that was designed for both educational and research purposes. It is a tool to facilitate the testing of new features, segmentations, machine learning approaches, and evaluation methods, by presenting a Matlab-like programming interface which illuminates the fundamental processes and algorithms in content based retrieval.

In the current state-of-the-art in multimedia content analysis (MCA), the fundamental techniques are typically derived from core pattern recognition and computer vision algorithms. It is well known that completely automatic pattern recognition and computer vision approaches have not been successful in being robust and domain independent so we should not expect more from MCA algorithms. The exception to this would naturally be methods which are human-interactive or not automatic. In this paper, we describe some of the recent work we have done in multimedia content analysis across multiple domains where the fundamental technique is founded in interactive search. Our novel algorithm integrates our previous work from wavelet based salient points and genetic algorithms and shows that the main contribution and improvement is from the user feedback provided by the interactive search.