MapReduce Paves the Way for CBIR
From the post:
Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries.
As a research team from the School of Science, Information Technology and Engineering at theUniversity of Ballarat, Australia has suggested, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately.
The researchers say that with this in mind, it has become increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the offer up a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment.
Reference to the paper: MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud by Sitalakshmi Venkatraman. (In Hybrid Intelligent Systems (HIS), 2012 12th International Conference on)
Abstract:
Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations.
Sounds like the basis for a user-augmented index of visual content to me.
You?