In this paper, we present an image parsing to text generation (I2T) framework that generates natural language descriptions from image and video content. This framework converts the harder content based image and video retrieval problem into an easier text search problem with potential applications in Internet search and visual data mining. The proposed I2T framework follows three steps. 1) Input images or video frames are decomposed into their constituent visual patterns through an image parsing engine, which outputs a scene as a parse graph representation, in a spirit similar to parsing sentences in speech and natural language. 2) The parse graphs are converted into semantic representation using the Web Ontology Language (OWL) format, which is a formal and unambiguous knowledge representation. 3) A text generation engine converts the semantic representation into a semantically meaningful, human readable and query-able text report. Success of the above framework relies on two knowledge bases. The first one is a visual knowledge base that provides top-down hypotheses for image parsing and serves as an image ontology for translating parse graphs into semantic representations. The core of the visual knowledge base is an And-Or graph representation. It entails vocabularies of visual elements including pixels, primitives, parts, objects and scenes and a stochastic image grammar specifying compositional, spatial, temporal and functional relations between visual elements. We developed a large-scale ground-truth image database and an interactive image annotation software to build the And-Or graph from real-world image instances. The second knowledge base is a general knowledge base that interconnects several domain specific ontologies in the form of the Semantic Web. This knowledge base further enriches the semantic representation of visual content with domain specific information. Finally, we demonstrate a case study in video surveillance, an end-to-end system that automatically infers video events and generates natural language descriptions of video scenes. Experiments with maritime and urban scenes indicate the feasibility of the proposed approach.

Natural images have a vast amount of visual patterns distributed in a wide spectrum of subspaces of varying complexities and dimensions. Understanding the characteristics of these subspaces and their compositional structures is of fundamental importance for pattern modeling, learning and recognition. In this paper, we start with small image patches and define two types of atomic subspaces: explicit manifolds of low dimensions for structural primitives and implicit manifolds of high dimensions for stochastic textures. Then we present an information theoretical learning framework that derives common models for these manifolds through information projection, and study a manifold pursuit algorithm that clusters image patches into those atomic subspaces and ranks them according to their information gains. We further show how those atomic subspaces change over an image scaling process and how they are composed to form larger and more complex image patterns. Finally, we integrate the implicit and explicit manifolds to form a primal sketch model as a generic representation in early vision and to generate a hybrid image template representation for object category recognition in high level vision. The study of the mathematical structures in the image space sheds lights on some basic questions in human vision, such as atomic elements in visual perception, the perceptual metrics in various manifolds, and the perceptual transitions over image scales.