Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected in the wild.The resulting Describable Textures Dataset (DTD) is the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio

Citizen science for cuneiform studies

This paper examines the potential applications of Citizen Science and Open Linked Data within a critical Web Science framework. Described here is a work-inprocess concerning an interdisciplinary, multiinstitutional project for the digitization, annotation and online dissemination of a large corpus of written material from ancient Mesopotamia. The paper includes an outline of the problems presented by a large, heterogeneous and incomplete dataset, as well as a discussion of the potential of Citizen Science as a potential solution, combining both technical and social aspects. Drawing inspiration from other successful Citizen Science projects, the current paper suggests a process for capturing and enriching the data in ways which can address not only the challenges of the current data set, but also similar issues arising elsewhere on the wider Web

Using association rule mining to enrich semantic concepts for video retrieval

In order to achieve true content-based information retrieval on video we should analyse and index video with high-level semantic concepts in addition to using user-generated tags and structured metadata like title, date, etc. However the range of such high-level semantic concepts, detected either manually or automatically, usually limited compared to the richness of information content in video and the potential vocabulary of available concepts for indexing. Even though there is work to improve the performance of individual concept classifiers, we should strive to make the best use of whatever partial sets of semantic concept occurrences are available to us. We describe in this paper our method for using association rule mining to automatically enrich the representation of video content through a set of semantic concepts based on concept co-occurrence patterns. We describe our experiments on the TRECVid 2005 video corpus annotated with the 449 concepts of the LSCOM ontology. The evaluation of our results shows the usefulness of our approach

Combining textual and visual information processing for interactive video retrieval: SCHEMA's participation in TRECVID 2004

In this paper, the two different applications based on the Schema Reference System that were developed by the SCHEMA NoE for participation to the search task of TRECVID 2004 are illustrated. The first application, named ”Schema-Text”, is an interactive retrieval application that employs only textual information while the second one, named ”Schema-XM”, is an extension of the former, employing algorithms and methods for combining textual, visual and higher level information. Two runs for each application were submitted, I A 2 SCHEMA-Text 3, I A 2 SCHEMA-Text 4 for Schema-Text and I A 2 SCHEMA-XM 1, I A 2 SCHEMA-XM 2 for Schema-XM. The comparison of these two applications in terms of retrieval efficiency revealed that the combination of information from different data sources can provide higher efficiency for retrieval systems. Experimental testing additionally revealed that initially performing a text-based query and subsequently proceeding with visual similarity search using one of the returned relevant keyframes as an example image is a good scheme for combining visual and textual information