Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.Comment: 11 pages, to appear at ACM MM'1

Multimedia Annotation Interoperability Framework

Multimedia systems typically contain digital documents of mixed media types, which are indexed on the basis of strongly divergent metadata standards. This severely hamplers the inter-operation of such systems. Therefore, machine understanding of metadata comming from different applications is a basic requirement for the inter-operation of distributed Multimedia systems. In this document, we present how interoperability among metadata, vocabularies/ontologies and services is enhanced using Semantic Web technologies. In addition, it provides guidelines for semantic interoperability, illustrated by use cases. Finally, it presents an overview of the most commonly used metadata standards and tools, and provides the general research direction for semantic interoperability using Semantic Web technologies

Semantic Tagging with Deep Residual Networks

We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71% accuracy on UD v1.2 and 95.67% accuracy on UD v1.3).Comment: COLING 2016, camera ready versio

Opportunities and challenges of geospatial analysis for promoting urban livability in the era of big data and machine learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement

Reverse spatial visual top-k query

With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top- $k$ query (RSVQ k ) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top- $k$ queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2 -Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2 -Tree, called CVR 2 -Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2 -Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models