Mining photographic collections to enhance the precision and recall of search results using semantically controlled query expansion

Driven by a larger and more diverse user-base and datasets, modern Information Retrieval techniques are striving to become contextually-aware in order to provide users with a more satisfactory search experience. While text-only retrieval methods are significantly more accurate and faster to render results than purely visual retrieval methods, these latter provide a rich complementary medium which can be used to obtain relevant and different results from those obtained using text-only retrieval. Moreover, the visual retrieval methods can be used to learn the user’s context and preferences, in particular the user’s relevance feedback, and exploit them to narrow down the search to more accurate results. Despite the overall deficiency in precision of visual retrieval result, the top results are accurate enough to be used for query expansion, when expanded in a controlled manner. The method we propose overcomes the usual pitfalls of visual retrieval: 1. The hardware barrier giving rise to prohibitively slow systems. 2. Results dominated by noise. 3. A significant gap between the low-level features and the semantics of the query. In our thesis, the first barrier is overcome by employing a simple block-based visual features which outperforms a method based on MPEG-7 features specially at early precision (precision of the top results). For the second obstacle, lists from words semantically weighted according to their degree of relation to the original query or to relevance feedback from example images are formed. These lists provide filters through which the confidence in the candidate results is assessed for inclusion in the results. This allows for more reliable Pseudo-Relevance Feedback (PRF). This technique is then used to bridge the third barrier; the semantic gap. It consists of a second step query, re-querying the data set with an query expanded with weighted words obtained from the initial query, and semantically filtered (SF) without human intervention. We developed our PRF-SF method on the IAPR TC-12 benchmark dataset of 20,000 tourist images, obtaining promising results, and tested it on the different and much larger Belga benchmark dataset of approximately 500,000 news images originating from a different source. Our experiments confirmed the potential of the method in improving the overall Mean Average Precision, recall, as well as the level of diversity of the results measured using cluster recall

Genome and transcriptome of the regeneration-competent flatworm, Macrostomum lignano.

The free-living flatworm, Macrostomum lignano has an impressive regenerative capacity. Following injury, it can regenerate almost an entirely new organism because of the presence of an abundant somatic stem cell population, the neoblasts. This set of unique properties makes many flatworms attractive organisms for studying the evolution of pathways involved in tissue self-renewal, cell-fate specification, and regeneration. The use of these organisms as models, however, is hampered by the lack of a well-assembled and annotated genome sequences, fundamental to modern genetic and molecular studies. Here we report the genomic sequence of M. lignano and an accompanying characterization of its transcriptome. The genome structure of M. lignano is remarkably complex, with ∼75% of its sequence being comprised of simple repeats and transposon sequences. This has made high-quality assembly from Illumina reads alone impossible (N50=222 bp). We therefore generated 130× coverage by long sequencing reads from the Pacific Biosciences platform to create a substantially improved assembly with an N50 of 64 Kbp. We complemented the reference genome with an assembled and annotated transcriptome, and used both of these datasets in combination to probe gene-expression patterns during regeneration, examining pathways important to stem cell function.This work is supported by National Institutes of Health Grants R37 GM062534 (to G.J.H.) and R01-HG006677 (to M.S.); National Science Foundation Grant DBI-1350041 (to M.S.); and a Swiss National Science Foundation Grant 31003A-143732 (to L.S.). This work was performed with assistance from Cold Spring Harbor Laboratory Shared Resources, which are funded, in part, by Cancer Center Support Grant 5P30CA045508.This is the final version of the article. It first appeared from PNAS via http://dx.doi.org/10.1073/pnas.151671811

A conceptual framework for adaptive multimedia presentations

In this thesis we propose a framework for a system that dynamically selects and plays multimedia files from a large data repository in order to produce a presentation. The presentation is generated based on the technical, semantic and relational textual annotation of the data as well as context-sensitive rules and patterns of selection discovered with the aid of the system during the preparation phase. We borrow concepts from the fields of discourse analysis and rhetorical structure as the theoretical basis of our work. To validate the framework, a prototype was developed using Java, Flash-MX and XML with data created and annotated by a research group from the Department of Design Art

Generating Adaptive Multimedia Presentations Based on a Semiotic Framework

Abstract. We propose a framework for generating adaptive multimedia presentations through the dynamic selection of files from a large data repository. The presentation is generated based on the technical(syntactic), semantic and relational textual annotation of the data as well as contextsensitive rules and patterns of selection discovered with the aid of the system during the preparation phase. We borrow concepts from the fields of discourse analysis and rhetorical structure as the theoretical basis of our work. To validate the framework, a prototype was developed using Java, Flash-MX and XML.