Post date: Mar 16, 2017 9:25:29 PM

Searchable Datasets in Python: Images across Domains, Experiments, Algorithms, and Learning

When you hear about searching, chances are the first image that pops into your mind is a web browser and a web search engine. From ancient Alta Vista to Google, querying capabilities using web crawlers and indexers have shaped the way we pursue and retrieve information: “If you don’t know something, you Google it.” As of 2017, this simple action comes with a caveat: there are billions of websites in the world wide web, totaling approximately half a zettabyte or 10^21 bytes = 1,000,000,000,000,000,000,000 bytes. If you are looking for popular items, such as the CD cover of your favorite band, you will often get to it in seconds. However, if you are looking for a specific scientific image, it may feel like looking for a needle in a haystack. This is why a team at BIDS decided to tackle this issue and create a tool tailored to scientific datasets lying in databases not immediately obvious in the WWW.

pyCBIR is a new python tool for content-based image retrieval (CBIR) capable of searching for relevant items in large databases given unseen samples. While much work in CBIR has targeted ads and recommendation systems, our pyCBIR allows general purpose investigation across image domains and experiments. Also, pyCBIR contains different distance metrics and several feature extraction techniques, including convolutional neural networks (CNN). More

Other events in 2017:

At the Federal University of Ouro Preto starting Nov 6th, 2017.

At the Federal University of Ceara starting June 24th, 2017.