Marcos Vinicius Mussel Cirne

Summarization of Videos by Image Quality Assessment

Video summarization plays a key role in manipulating large amounts of digital videos, making it faster to analyze their contents and aiding in the tasks of browsing, indexing and retrieval. A straightforward method for producing the summaries is by means of extraction of color features from the video frames. However, in order to automatically generate summaries as human beings would do, the way that humans perceive images must be considered, which can be done by image quality assessment (IQA) metrics. This work presents VSQUAL, a method for summarization of videos based on objective IQA metrics, which is also used for other purposes such as shot boundary detection and keyframe extraction. Results of the proposed method are compared against other approaches of the literature with a specific evaluation metric.

VISCOM: A robust video summarization approach using color co-occurrence matrices

Video summarization techniques have allowed the content analysis of large volumes of digital video sequences of different categories, such as movies, documentaries, lectures, sports, surveillance, and news. This paper proposes and evaluates a novel video summarization approach called VISCOM, which is based on color co-occurrence matrices to describe the video frames and generate a synopsis with the most representative frames. Experiments conducted on two different data sets of various genres demonstrate the effectiveness of the proposed method in terms of quality. The resulting video summaries are compared against several others using a specific quantitative evaluation metric, producing competitive outcomes among the evaluated methods.

A Video Summarization Method Based on Spectral Clustering

The constant increase in the availability of digital videos has demanded the development of techniques capable of managing these data in a faster and more efficient way, especially concerning the content analysis. One of the research areas that have recently evolved significantly at this point is video summarization, which consists of generating a short version of a certain video, such that the users can grasp the central message transmitted by the original video. Many of the video summarization approaches make use of clustering algorithms, with the goal of extracting the most important frames of the videos to compose the final summary. However, special clustering algorithms based on a spectral approach have obtained superior results than those obtained with classical clustering algorithms, not only in video summarization techniques but also in other fields, such as machine learning, pattern recognition, and data mining. This work proposes a method for summarization of videos, regardless of their genre, using spectral clustering algorithms. Possibilities of algorithm parallelization for the purpose of optimizing the general performance of the proposed methodology are also discussed.

Marching cubes technique for volumetric visualization accelerated with graphics processing units

Volume visualization has numerous applications that benefit different knowledge domains, such as biology, medicine, meteorology, oceanography, geology, among others. With the continuous advances of technology, it has been possible to achieve considerable rendering rates and a high degree of realism. Visualization tools have currently assisted users with the visual analysis of complex and large datasets. Marching cubes is one of the most widely used real-time volume rendering methods. This paper describes a methodology for speeding up the marching cubes algorithm on a graphics processing unit and discusses a number of ways to improve its performance by means of auxiliary spatial data structures. Experiments conducted with use of several volumetric datasets demonstrate the effectiveness of the developed method.

Combination of Texture and Geometric Features for Age Estimation in Face Images.

Automatic age estimation from facial images has recently received an increasing interest due to a variety of applications, such as surveillance, human-computer interaction, forensics, and recommendation systems. Despite such advances, age estimation remains an open problem due to several challenges associated with the aging process. In this work, we develop and analyze an automatic age estimation method from face images based on a combination of textural and geometric features. Experiments are conducted on the Adience dataset (Adience Benchmark, 2017; Eidinger et al., 2014), a large known benchmark used to evaluate both age and gender classification approaches.

Partial Least Squares Image Clustering

Clustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis. This work presents a novel image clustering method called Partial Least Square Image Clustering (PLSIC), which employs a one against-all Partial Least Squares classifier to find image clusters with low redundancy (each cluster represents different visual concept) and high purity (two visual concepts should not be in the same cluster). The main goal of the proposed approach is to find groups of images in an arbitrary set of unlabeled images to convey well defined visual concepts. As a case study, we evaluate the PLSIC to the video summarization problem by means of experiments with 50 videos from various genres of the Open Video Project, comparing summaries generated by the PLSIC with other video summarization approaches found in the literature. A experimental evaluation demonstrates that the proposed method can produce very satisfactory results.