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A method is presented to provide a useful searchable index for spoken audio documents. The task differs from the traditional (text) document indexing, because large audio databases are decoded by automatic speech recognition and decoding errors occur frequ ...
We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming equal relevance for the text and visual modalities, we propose a new way of m ...
Semantic document annotation may be useful for many tasks. In particular, in the framework of the MDM project(http://www.issco.unige.ch/projects/im2/mdm/), topical annotation -- i.e. the annotation of document segments with tags identifying the topics disc ...
We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming equal relevance for the text and visual modalities, we propose a new way of m ...
We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Our formulation provides answers to three open questions:(1) whether the invariant local features are suitable f ...
This paper describes a new latent semantic indexing (LSI) method for spoken audio documents. The framework is indexing broadcast news from radio and TV as a combination of large vocabulary continuous speech recognition (LVCSR), natural language processing ...
Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). Th ...
Documents are usually represented in the bag-of-word space. However, this representation does not take into account the possible relations between words. We propose here a graphical model for representing documents: the Theme Topic Mixture Model (TTMM). Th ...
In this article we present a novel approach of integrating textual and visual descriptors of images in a unified retrieval structure. The methodology, inspired from text retrieval and information filtering is based on Latent Semantic Indexing (LS1). ...
This paper presents an indexing system for spoken audio documents. The framework is indexing and retrieval of broadcast news. The proposed indexing system applies latent semantic analysis (LSA) and self-organizing maps (SOM) to map the documents into a sem ...
