Image-based Recommendations on Styles and Substitutes

Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201

Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Hypermedia Learning Objects System - On the Way to a Semantic Educational Web

While eLearning systems become more and more popular in daily education, available applications lack opportunities to structure, annotate and manage their contents in a high-level fashion. General efforts to improve these deficits are taken by initiatives to define rich meta data sets and a semanticWeb layer. In the present paper we introduce Hylos, an online learning system. Hylos is based on a cellular eLearning Object (ELO) information model encapsulating meta data conforming to the LOM standard. Content management is provisioned on this semantic meta data level and allows for variable, dynamically adaptable access structures. Context aware multifunctional links permit a systematic navigation depending on the learners and didactic needs, thereby exploring the capabilities of the semantic web. Hylos is built upon the more general Multimedia Information Repository (MIR) and the MIR adaptive context linking environment (MIRaCLE), its linking extension. MIR is an open system supporting the standards XML, Corba and JNDI. Hylos benefits from manageable information structures, sophisticated access logic and high-level authoring tools like the ELO editor responsible for the semi-manual creation of meta data and WYSIWYG like content editing.Comment: 11 pages, 7 figure

Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search

Textual and structural approaches to detecting figure plagiarism in scientific publications

The figures play important role in disseminating important ideas and findings which enable the readers to understand the details of the work. The part of figures in understanding the details of the documents increase more use of them, which have led to a serious problem of taking other peoples’ figures without giving credit to the source. Although significant efforts have been made in developing methods for estimating pairwise diagram figure similarity, there are little attentions found in the research community to detect any of the instances of figure plagiarism such as manipulating figures by changing the structure of the figure, inserting, deleting and substituting the components or when the text content is manipulated. To address this gap, this project compares theeffectiveness of the textual and structural representations of techniques to support the figure plagiarism detection. In addition to these two representations, the textual comparison method is designed to match the figure contents based on a word-gram representation using the Jaccard similarity measure, while the structural comparison method is designed to compare the text within the components as well as the relationship between the components of the figures using graph edit distance measure. These techniques are experimentally evaluated across the seven instances of figure plagiarism, in terms of their similarity values and the precision and recall metrics. The experimental results show that the structural representation of figures slightly outperformed the textual representation in detecting all the instances of the figure plagiarism