• Corpus ID: 41683372

Developing a Dataset for Evaluating Approaches for Document Expansion with Images

@inproceedings{Ganguly2016DevelopingAD,
  title={Developing a Dataset for Evaluating Approaches for Document Expansion with Images},
  author={Debasis Ganguly and Iacer Calixto and G. Jones},
  booktitle={LREC},
  year={2016}
}
Motivated by the adage that a “picture is worth a thousand words” it can be reasoned that automatically enriching the textual content of a document with relevant images can increase the readability of a document. Moreover, features extracted from the additional image data inserted into the textual content of a document may, in principle, be also be used by a retrieval engine to better match the topic of a document with that of a given query. In this paper, we describe our approach of… 
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References

SHOWING 1-10 OF 11 REFERENCES

A Language Modeling Approach to Information Retrieval

TLDR
It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection and provide further proof of concept for the use of language models for retrieval tasks.

Overview of the Wikipedia Retrieval Task at ImageCLEF 2010

TLDR
This paper presents an overview of the resources, topics, and assessments of the Wikipedia Retrieval task at ImageCLEF 2010, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results.

Deep Visual-Semantic Alignments for Generating Image Descriptions

  • A. KarpathyLi Fei-Fei
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2017
TLDR
A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.

ImageCLEF 2014: Overview and Analysis of the Results

TLDR
The tasks and the 2014 competition are described, giving a unifying perspective of the present activities of the ImageCLEF lab while discussing future challenges and opportunities.

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

TLDR
An attention based model that automatically learns to describe the content of images is introduced that can be trained in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound.

Overview of the Eighth Text REtrieval Conference (TREC-8)

TLDR
The eighth Text REtrieval Conference TREC was held at the National Institute of Standards and Tech nology NIST on November and outlined the goals of the series of workshops designed to foster research in text retrieval.

Combination of Multiple Searches

TLDR
This paper describes one method that has been shown to increase performance by combining the similarity values from five different retrieval runs using both vector space and P-norm extended boolean retrieval methods.

Show and tell: A neural image caption generator

TLDR
This paper presents a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.

Topic Models for Image Annotation and Text Illustration

TLDR
A probabilistic model based on the assumption that images and their co-occurring textual data are generated by mixtures of latent topics is described, which outperforms previously proposed approaches when applied to image annotation and the related task of text illustration despite the noisy nature of the dataset.

Enabling the Discovery of Digital Cultural Heritage Objects through Wikipedia

TLDR
This paper proposes a novel mechanism for introducing new users to the items in a collection by allowing them to browse Wikipedia articles, which are augmented with items from the cultural heritage collection.