A Sketch-based Rapid Modeling Method for Crime Scene Presentation

The reconstruction of crime scene plays an important role in digital forensic application. This article integrates computer graphics, sketch-based retrieval and virtual reality (VR) techniques to develop a low-cost and rapid 3D crime scene presentation approach, which can be used by investigators to analyze and simulate the criminal process. First, we constructed a collection of 3D models for indoor crime scenes using various popular techniques, including laser scanning, image-based modeling and geometric modeling. Second, to quickly obtain an object of interest from the 3D model database, a sketch-based retrieval method was proposed. Finally, a rapid modeling system that integrates our database and retrieval algorithm was developed to quickly build a digital crime scene. For practical use, an interactive real-time virtual roaming application was developed in Unity 3D and a low-cost VR head-mounted display (HMD). Practical cases have been implemented to demonstrate the feasibility and availability of our method

LiveSketch: Query Perturbations for Guided Sketch-based Visual Search

LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-shot process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.Comment: Accepted to CVPR 201

Interactive retrieval of video using pre-computed shot-shot similarities

A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision