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. ^

Investigating Miami English-Spanish Bilinguals' Treatment of English Deictic Verbs of Motion

This investigation focused on the treatment of English deictic verbs of motion by Spanish-English bilinguals in Miami. Although English and Spanish share significant overlap of the spatial deixis system, they diverge in important aspects. It is not known how these verbs are processed by bilinguals. Thus, this study examined Spanish-English bilinguals’ interpretation of the verbs come, go, bring, and take in English. Forty-five monolingual English speakers and Spanish-English bilinguals participated. Participants were asked to watch video clips depicting motion events and to judge the acceptability of accompanying narrations spoken by the actors in the videos. Analyses showed that, in general, monolinguals and bilinguals patterned similarly across the deictic verbs come, bring, go and take. However, they did differ in relation to acceptability of word order for verbal objects. Also, bring was highly accepted by all language groups across all goal paths, possibly suggesting an innovation in its use.

A descriptive study of the ratio of approval to disapproval demonstrated by music teachers and classroom teachers in first, second, and third grades

The purpose of this study was to determine the approval to disapproval ratios of feedback given by music and classroom teachers to first, second and third grades. Eight teachers from a South Florida Elementary School were selected for this study. Twelve 20-minute videos were taken for further examination. Analyses of data using percentage formulas were used to determine the ratio of each of the teacher reinforcement. Classroom teachers gave 2.3% social approval feedback, 59% academic approval feedback, 22% social disapproval feedback, 16.5% academic disapproval feedback, and 0% errors. Music teachers gave .7% social approval feedback, 67% academic approval feedback, 22% social disapproval feedback, 10% academic disapproval feedback, and 0% errors. Today's teachers are 8% more academically approving than thirty years ago. Results also show that today's music teachers are still more approving than classroom teachers.