figumare

Brought to you by: alvarfr

Add a Review

Downloads: 0 This Week

Last Update: 2013-04-26

Download

Get an email when there's a new version of figumare

Windows Mac Linux BSD ChromeOS

Open Figurative Mark Retrieval.
Web Application that wraps "Lire" ( Java Image Retrieval Library ) Meant for retrieving a logo from a collection of registered logos.
Written in Groovy/Grails

Project Samples

Project Activity

See All Activity >

License

European Union Public License

Follow figumare

figumare Web Site

Other Useful Business Software

AI-powered service management for IT and enterprise teams

Enterprise-grade ITSM, for every business

Give your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.

Try it Free

Rate This Project

User Reviews

Be the first to post a review of figumare!

Additional Project Details

User Interface

Web-based

Programming Language

Groovy, Java

Registered

2010-08-23

Report inappropriate content

Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.

Try Free

Recommended Projects

userDefineLang do Notepad++
userDefineLang.xml da linguagem Groovy e Grails.
OpenLabFramework
Find us on github! https://github.com/NanoCAN/OpenLabFramework
Micronaut
Micronaut Application Framework
CNN for Image Retrieval
cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. This resource serves as both an educational reference and a foundation for further exploration in image retrieval research.
gensim
Topic Modelling for Humans