Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

YesThe exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.Supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

Comparative Study of Human Age Estimation with or without Preclassification of Gender and Facial Expression

Age estimation has many useful applications, such as age-based face classification, finding lost children, surveillance monitoring, and face recognition invariant to age progression. Among many factors affecting age estimation accuracy, gender and facial expression can have negative effects. In our research, the effects of gender and facial expression on age estimation using support vector regression (SVR) method are investigated. Our research is novel in the following four ways. First, the accuracies of age estimation using a single-level local binary pattern (LBP) and a multilevel LBP (MLBP) are compared, and MLBP shows better performance as an extractor of texture features globally. Second, we compare the accuracies of age estimation using global features extracted by MLBP, local features extracted by Gabor filtering, and the combination of the two methods. Results show that the third approach is the most accurate. Third, the accuracies of age estimation with and without preclassification of facial expression are compared and analyzed. Fourth, those with and without preclassification of gender are compared and analyzed. The experimental results show the effectiveness of gender preclassification in age estimation

Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model

Fantastic Extrapolations: An Exploratory Bibliometric Investigation into the Historic Development of English-Language Fantasy and Science Fiction Scholarship Through Fifty Years of Extrapolation

There are relatively few bibliometric or citation analysis historical studies of the scholarly literature of arts or humanities fields as compared with the science, technical, medical, or social science disciplines; many studies focus on the journal literature and use the formal works cited reference lists as captured by citation indexes as the basis for their conclusions. This study looks at aspects of the scholarship of the literary and media-based popular culture field of fantasy and science fiction (F&SF; aka: fantastic, fantastika) studies through the first 50 years of Extrapolation (December 1959-Fall 2009), the oldest continuing scholarly journal in the field, in three areas: -- History and editorial purpose, types of contributions, and recognition by general-, literature-, and F&SF-focused indexing services; -- Analyses of the 785 scholars published in the journal, by gender, co-authorship, affiliation and status (geographical, institutional, ranks, disciplines, awards), their referencing practices, and identification of the 55 most frequently published scholars; and, -- Analyses of more than 15,000 references given to 2,035 primary (creative) authors and more than 8,000 individual creative works, including collaboratively authored media, religious, and other titles, by gender and national affiliation, and by types of works. publication sources, language, and ages/dates, as found in 937 articles by 656 different authors. The primary references analyzed come not only from the traditional bibliometric locations in Works Cited lists, but also from Notes, and the references found in the rarely if ever studied informal locations (implicit citations), primarily within the text of the articles. The most frequently referenced primary authors and works are identified: 118 authors (20-563 references), beginning with Ursula K. Le Guin (563 references; 105 different works), Robert A. Heinlein (519; 90), and H. G. Wells (328; 52); 182 primary (creative) works (10-191 references), starting with Star Trek: The Original Series (191 references), Star Trek: The Next Generation (106), George Orwell’s Nineteen Eighty-Four (77), Wells’ The Time Machine (73), and Mary Shelley’s Frankenstein (71). This study should interest historians of arts and humanities scholarship, F&SF scholars, and librarians and archivists responsible for collection development and collections management in the areas of literature and media

Skin texture features for face recognition

Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm