Considering the two-dimensional (2D) imaging result is the map of the target on the imaging plane, the three-dimensional (3D) scattering distribution of the air target is reconstructed by comparing the 2D imaging results and the mapping images in this paper. These 2D images and mapping images are obtained by the radars which are located in different places but have the similar perspectives. Firstly, the mapping formulas are presented briefly. Then the influence of the radar distribution on the scattering distribution reconstruction is analyzed and the radar distribution mode for ensuring the similar perspectives and the scatterers reconstruction performance is established. Finally, the 3D scattering distribution reconstruction method is proposed. In the simulation experiments, the scattering distribution of the target is reconstructed and the validity of the proposed method is verified.

Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneficial for the retrieval of high-level events using visual examples, but without examples it is still hard to (1) determine which concepts are useful to pre-train ( Vocabulary challenge ) and (2) which pre-trained concept detectors are relevant for a certain unseen high-level event ( Concept Selection challenge ). In our article, we present our Semantic Event Retrieval System which (1) shows the importance of high-level concepts in a vocabulary for the retrieval of complex and generic high-level events and (2) uses a novel concept selection method ( i-w2v ) based on semantic embeddings. Our experiments on the international TRECVID Multimedia Event Detection benchmark show that a diverse vocabulary including high-level concepts improves performance on the retrieval of high-level events in videos and that our novel method outperforms a knowledge-based concept selection method.

Complex video event detection without visual examples is a very challenging issue in multimedia retrieval. We present a state-of-the-art framework for event search without any need of exemplar videos and textual metadata in search corpus. To perform event search given only query words, the core of our framework is a large, pre-built bank of concept detectors which can understand the content of a video in the perspective of object, scene, action and activity concepts. Leveraging such knowledge can effectively narrow the semantic gap between textual query and the visual content of videos. Besides the large concept bank, this paper focuses on two challenges that largely affect the retrieval performance when the size of the concept bank increases: (1) How to choose the right concepts in the concept bank to accurately represent the query; (2) if noisy concepts are inevitably chosen, how to minimize their influence. We share our novel insights on these particular problems, which paves the way for a practical system that achieves the best performance in NIST TRECVID 2015.

Retrieval of a complex multimedia event has long been regarded as a challenging task. Multimedia event recounting, other than event detection, focuses on providing comprehensible evidence which justifies a detection result. Recounting enables "video skimming", which not only enhances video exploration, but also makes human-in-the-loop possible for improving the detection result. Most existing systems treat event recounting as a disjoint post-processing step over the result of event detection. Unlike these systems, this doctoral research aims to provide an in-depth understanding of how recounting, i.e., evidence localization, helps in event detection in the first place. It can potentially benefit the overall design of an efficient event detection system with or without human-in-the-loop. More importantly, we propose a framework for detecting and recounting everyday events without any needs of training examples. The system only takes a text description of an event as input, then performs evidence localization, event detection and recounting in a large, unlabelled video corpus. The goal of the system is to take advantage of event recounting which eventually improves zero-example event detection. We present preliminary results and work in progress.

Attribute-based image representation, which represents an image by projecting it into a space spanned by attributes, has attracted increasing attention from both computer vision and multimedia communities for its compactness and potential to bridge the semantic gap. While many works focus on learning attribute models and utilizing them in image recognition and retrieval, few touch on the problem of how to effectively construct a vocabulary of attributes, which is an essential part of effective attribute-based representation. Most existing approaches define the attribute vocabulary by human experts or through existing ontology, which is often limited in coverage of general concept space. In this paper, we propose automatically constructing the attribute vocabulary by mining latent topics from the click-through log of a commercial image search engine. These attributes are referred to as latent topic attributes (LTA), which take advantage of tens of millions of interactions between user submitted queries and images, thereby providing better coverage for the concept space than existing approaches. The mining of latent topics from the click log is formulated as a matrix factorization problem, and further improved by weighted terms-based matrix factorization to address the extreme sparsity of the click-through matrix. Both qualitative results of the mined LTA and quantitative results on the standard image recognition benchmark demonstrate the mined LTA's effectiveness.