Automatic generation of effective video summaries

As the popularity of video as an information medium rises, the amount of video content that we produce and archive keeps growing. This creates a demand for shorter representations of videos in order to assist the task of video retrieval. The traditional solution is to let humans watch these videos and write textual summaries based on what they saw. This summarisation process, however, is time-consuming. Moreover, a lot of useful audio-visual information contained in the original video can be lost. Video summarisation aims to turn a full-length video into a more concise version that preserves as much information as possible. The problem of video summarisation is to minimise the trade-off between how concise and how representative a summary is. There are also usability concerns that need to be addressed in a video summarisation scheme. To solve these problems, this research aims to create an automatic video summarisation framework that combines and improves on existing video summarisation techniques, with the focus on practicality and user satisfaction. We also investigate the need for different summarisation strategies in different kinds of videos, for example news, sports, or TV series. Finally, we develop a video summarisation system based on the framework, which is validated by subjective and objective evaluation. The evaluation results shows that the proposed framework is effective for creating video skims, producing high user satisfaction rate and having reasonably low computing requirement. We also demonstrate that the techniques presented in this research can be used for visualising video summaries in the form web pages showing various useful information, both from the video itself and from external sources.

Determinants of rapid weight gain during infancy: Baseline results from the NOURISH randomised controlled trial.

Background: Rapid weight gain in infancy is an important predictor of obesity in later childhood. Our aim was to determine which modifiable variables are associated with rapid weight gain in early life. Methods: Subjects were healthy infants enrolled in NOURISH, a randomised, controlled trial evaluating an intervention to promote positive early feeding practices. This analysis used the birth and baseline data for NOURISH. Birthweight was collected from hospital records and infants were also weighed at baseline assessment when they were aged 4-7 months and before randomisation. Infant feeding practices and demographic variables were collected from the mother using a self administered questionnaire. Rapid weight gain was defined as an increase in weight-for-age Z-score (using WHO standards) above 0.67 SD from birth to baseline assessment, which is interpreted clinically as crossing centile lines on a growth chart. Variables associated with rapid weight gain were evaluated using a multivariable logistic regression model. Results: Complete data were available for 612 infants (88% of the total sample recruited) with a mean (SD) age of 4.3 (1.0) months at baseline assessment. After adjusting for mother's age, smoking in pregnancy, BMI, and education and infant birthweight, age, gender and introduction of solid foods, the only two modifiable factors associated with rapid weight gain to attain statistical significance were formula feeding [OR=1.72 (95%CI 1.01-2.94), P= 0.047] and feeding on schedule [OR=2.29 (95%CI 1.14-4.61), P=0.020]. Male gender and lower birthweight were non-modifiable factors associated with rapid weight gain. Conclusions: This analysis supports the contention that there is an association between formula feeding, feeding to schedule and weight gain in the first months of life. Mechanisms may include the actual content of formula milk (e.g. higher protein intake) or differences in feeding styles, such as feeding to schedule, which increase the risk of overfeeding. Trial Registration: Australian Clinical Trials Registry ACTRN12608000056392

Psycho-markers of weight loss. The roles of TFEQ Disinhibition and Restraint in exercise-induced weight management

Eating behaviour traits, namely Disinhibition and Restraint, have the potential to exert an effect on food intake and energy balance. The effectiveness of exercise as a method of weight management could be influenced by these traits. Fifty eight overweight and obese participants completed 12-weeks of supervised exercise. Each participant was prescribed supervised exercise based on an expenditure of 500 kcal/session, 5 d/week for 12-weeks. Following 12-weeks of exercise there was a significant reduction in mean body weight (-3.26 ± 3.63 kg), fat mass (FM: -3.26 ± 2.64 kg), BMI (-1.16 ± 1.17 kg/m2)and waist circumference (WC: -5.0 ± 3.23 cm). Regression analyses revealed a higher baseline Disinhibition score was associated with a greater reduction in BMI and WC, while Internal Disinhibition was associated with a larger decrease in weight, %FM and WC. Neither baseline Restraint or Hunger were associated with any of the anthropometric markers at baseline or after 12-weeks. Furthermore, after 12-weeks of exercise, a decrease in Disinhibition and increase in Restraint were associated with a greater reduction in WC, whereas only Restraint was associated with a decrease in weight. Post-hoc analysis of the sub-factors revealed a decrease in External Disinhibition and increase in Flexible Restraint were associated with weight loss. However, an increase in Rigid Restraint was associated with a reduction in %FM and WC. These findings suggest that exercise-induced weight loss is more marked in individuals with a high level of Disinhibition. These data demonstrate the important roles that Disinhibition and Restraint play in the relationship between exercise and energy balance.

Towards an automatic system for road lane marking extraction in large-scale aerial images acquired over rural areas by hierarchical image analysis and Gabor filter

An automatic approach to road lane marking extraction from high-resolution aerial images is proposed, which can automatically detect the road surfaces in rural areas based on hierarchical image analysis. The procedure is facilitated by the road centrelines obtained from low-resolution images. The lane markings are further extracted on the generated road surfaces with 2D Gabor filters. The proposed method is applied on the aerial images of the Bruce Highway around Gympie, Queensland. Evaluation of the generated road surfaces and lane markings using four representative test fields has validated the proposed method.

Weight management and its role in breast cancer rehabilitation

Overweight and obesity are risk factors for post-menopausal breast cancer, and many women diagnosed with breast cancer, irrespective of menopausal status, gain weight after diagnosis. Weight management plays an important role in rehabilitation and recovery since obesity and/or weight gain may lead to poorer breast cancer prognosis, as well as prevalent co-morbid conditions (e.g. cardiovascular disease and diabetes), poorer surgical outcomes (e.g., increased operating and recovery times, higher infection rates, and poorer healing), lymphedema, fatigue, functional decline, and poorer health and overall quality of life. Health care professionals should encourage weight management at all phases of the cancer care continuum as a means to potentially avoid adverse sequelae and late effects, as well as to improve overall health and possibly survival. Comprehensive approaches that involve dietary and behavior modification, and increased aerobic and strength training exercise have shown promise in either preventing weight gain or promoting weight loss, reducing biomarkers associated with inflammation and co-morbidity, and improving lifestyle behaviors, functional status, and quality of life in this high-risk patient population.

Acoustic component detection for automatic species recognition in environmental monitoring

Automatic species recognition plays an important role in assisting ecologists to monitor the environment. One critical issue in this research area is that software developers need prior knowledge of specific targets people are interested in to build templates for these targets. This paper proposes a novel approach for automatic species recognition based on generic knowledge about acoustic events to detect species. Acoustic component detection is the most critical and fundamental part of this proposed approach. This paper gives clear definitions of acoustic components and presents three clustering algorithms for detecting four acoustic components in sound recordings; whistles, clicks, slurs, and blocks. The experiment result demonstrates that these acoustic component recognisers have achieved high precision and recall rate.

Evaluating automatic road detection across a large aerial imagery collection

The automated extraction of roads from aerial imagery can be of value for tasks including mapping, surveillance and change detection. Unfortunately, there are no public databases or standard evaluation protocols for evaluating these techniques. Many techniques are further hindered by a reliance on manual initialisation, making large scale application of the techniques impractical. In this paper, we present a public database and evaluation protocol for the evaluation of road extraction algorithms, and propose an improved automatic seed finding technique to initialise road extraction, based on a combination of geometric and colour features.

Investigating the performance of automatic new topic identification across multiple datasets 1

Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures.

Towards automatic object segmentation with sequential multiple views

Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.