A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Everglades Ridge, Slough, and Tree Island Mosaics: Year 2 Annual Report

Status and history of the Ridge-Slough Mosaic The Florida Everglades is a large subtropical wetland with diverse hydrologic, edaphic, and vegetative characteristics. Historically, a significant portion of this system was a slow moving river originating from the Kissimmee River floodplain, flowing into the vast but shallow Lake Okeechobee, and draining south-southwest over extensive peatlands into Florida Bay (McVoy 2011). Human-induced alterations to the hydrologic regime, including reduction, stabilization, and impoundment of water flow through diversion and compartmentalization of water via canals and levees have degraded pre-drainage vegetation patterns and microtopographic structure (Davis and Ogden 1994, Ogden 2005, McVoy 2011). The Everglades peatland emerged 5,000 years ago with the stabilization of sea level at approximately current elevations (Loveless 1959, Gleason and Stone 1994). This, combined with subtropical rainfalls, allowed a vast mass of water to slowly flow over a limestone bedrock platform 160 km long and 50 km wide at a near uniform descent totaling about 6 m, ultimately reaching Florida Bay (Stephens 1956, Gleason and Stone 1994, McVoy 2011). Vegetation quickly colonized the area, and peat, in the absence of adequate respiration, accumulated on the limestone bedrock to a depth of 3-3.7 m (Gleason and Stone 1994, McVoy et al. 2011). The “River of Grass” referenced by Douglas (1947) alludes to the dually intertwined processes of the historic riverine nature of the Everglades and the vast sawgrass (Cladium jamaicense) communities that have dominated the landscape for about the last 1,000 years (Bernhardt and Willard 2009).