Image annotation with Photocopain

Photo annotation is a resource-intensive task, yet is increasingly essential as image archives and personal photo collections grow in size. There is an inherent conflict in the process of describing and archiving personal experiences, because casual users are generally unwilling to expend large amounts of effort on creating the annotations which are required to organise their collections so that they can make best use of them. This paper describes the Photocopain system, a semi-automatic image annotation system which combines information about the context in which a photograph was captured with information from other readily available sources in order to generate outline annotations for that photograph that the user may further extend or amend

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings

Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design

published or submitted for publicatio

Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis

User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop

Modeling Visual Rhetoric and Semantics in Multimedia

Recent advances in machine learning have enabled computer vision algorithms to model complicated visual phenomena with accuracies unthinkable a mere decade ago. Their high-performance on a plethora of vision-related tasks has enabled computer vision researchers to begin to move beyond traditional visual recognition problems to tasks requiring higher-level image understanding. However, most computer vision research still focuses on describing what images, text, or other media literally portrays. In contrast, in this dissertation we focus on learning how and why such content is portrayed. Rather than viewing media for its content, we recast the problem as understanding visual communication and visual rhetoric. For example, the same content may be portrayed in different ways in order to present the story the author wishes to convey. We thus seek to model not only the content of the media, but its authorial intent and latent messaging. Understanding how and why visual content is portrayed a certain way requires understanding higher level abstract semantic concepts which are themselves latent within visual media. By latent, we mean the concept is not readily visually accessible within a single image (e.g. right vs left political bias), in contrast to explicit visual semantic concepts such as objects. Specifically, we study the problems of modeling photographic style (how professional photographers portray their subjects), understanding visual persuasion in image advertisements, modeling political bias in multimedia (image and text) news articles, and learning cross-modal semantic representations. While most past research in vision and natural language processing studies the case where visual content and paired text are highly aligned (as in the case of image captions), we target the case where each modality conveys complementary information to tell a larger story. We particularly focus on the problem of learning cross-modal representations from multimedia exhibiting weak alignment between the image and text modalities. A variety of techniques are presented which improve modeling of multimedia rhetoric in real-world data and enable more robust artificially intelligent systems