Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations included in each domain. We provide the first dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations. We also contribute the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performance of the attribute based approach, we also find interpretable features that are in accordance with the predictions from social psychology literature. Beyond our findings, we believe that our contributions more tightly interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.
Social relation is a significant part of social network study @cite_15 @cite_10 @cite_42 . This section focuses on the related work in computer vision while the next section outlines different theories in the psychology literature.
@cite_15 @cite_42 @cite_10
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Part 1: Social Analysis, Discourse Analysis, Text Analysis 1. Introduction 2. Texts, Social Events, and Social Practices 3. Intertextuality and Assumptions Part 2: Genres and Action 4. Genres 5. Meaning Relations between Sentences and Clauses 6. Types of Exchange, Speech Functions, and Grammatical Mood Part 3: Discourses and Representations 7. Discourses 8. Representations of Social Events Part 4: Styles and Identities 9. Styles 10. Modality and Evaluation 11. Conclusion Photos are an important information carrier for implicit relationships. In this article, we introduce an image based social network, called CelebrityNet, built from implicit relationships encoded in a collection of celebrity images. We analyze the social properties reflected in this image-based social network and automatically infer communities among the celebrities. We demonstrate the interesting discoveries of the CelebrityNet. We particularly compare the inferred communities with human manually labeled ones and show quantitatively that the automatically detected communities are highly aligned with that of human interpretation. Inspired by the uniqueness of visual content and tag concepts within each community of the CelebrityNet, we further demonstrate that the constructed social network can serve as a knowledge base for high-level visual recognition tasks. In particular, this social network is capable of significantly improving the performance of automatic image annotation and classification of unknown images. We introduce a method for extracting the social network structure for the persons appearing in a set of video clips. Individuals are unknown, and are not matched against known enrollments. An identity cluster representing an individual is formed by grouping similar-appearing faces from different videos. Each identity cluster is represented by a node in the social network. Two nodes are linked if the faces from their clusters appeared together in one or more video frames. Our approach incorporates a novel active clustering technique to create more accurate identity clusters based on feedback from the user about ambiguously matched faces. The final output consists of one or more network structures that represent the social group(s), and a list of persons who potentially connect multiple social groups. Our results demonstrate the efficacy of the proposed clustering algorithm and network analysis techniques.