We gave a tutorial on “Image Tag Assignment, Refinement and Retrieval” at ACM MM 2015, based on our recent survey. Our tutorial focuses on challenges and solutions for content-based image retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems: tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, and recognize their merits and limitations.
We provided also an hands-on session with the main methods, software and datasets. All data, code and slides are online at: http://www.micc.unifi.it/tagsurvey
I have just given a tutorial on kNN at the Stanford Artificial Intelligence Laboratory’s Outreach Summer program (SAILORS). SAILORS is designed to expose high school students in underrepresented populations to the field of Artificial Intelligence.
The slides are available on this page and the Matlab code is also available for download. This is an updated version of the code used in class and should work also on Octave.
Lorenzo Seidenari and I gave the tutorial “Hands on Advanced Bag-of-Words Models for Visual Recognition” at the ICPR 2014 conference (August 24, Stockholm, Sweden).
All materials – i.e. slides, Matlab code, images and features – and more details can still be found on this webpage.
University of Florence
Course on Multimedia Databases – 2013/14 (Prof. A. Del Bimbo)
Instructors: Lamberto Ballan and Lorenzo Seidenari
Goal
The goal of this laboratory is to get basic practical experience with image classification. We will implement a system based on bag-of-visual-words image representation and will apply it to the classification of four image classes: airplanes, cars, faces, and motorbikes.
We will follow the three steps:
- Load pre-computed image features, construct visual dictionary, quantize features
- Represent images by histograms of quantized features
- Classify images with Nearest Neighbor / SVM classifiers
Getting started
- Download excercises-description.pdf
- Download lab-bow.zip (type the password given in class to uncompress the file) including the Matlab code
- Download 4_ObjectCategories.zip including images and precomputed SIFT features; uncompress this file in lab-bow/img
- Download 15_ObjectCategories.zip including images and precomputed SIFT features; uncompress this file in lab-bow/img
- Start Matlab in the directory lab-bow/matlab and run exercises.m
MICC laboratories, Florence, 31th October 2013 (10.15-13.15). Course on Multimedia Databases (DBMM) – laboratory lecture.
- Goal: logo recognition in web images.
- Dataset/testset: find 4 different logos vs 110 images.
- Evaluation metrics: recognition performances will be evaluated in terms of mean Average Precision (mAP).
Instructors: Lamberto Ballan, Lorenzo Seidenari.
Download Software & Dataset 
(* based on VLFeat library by A. Vedaldi)
Final results (ranking): http://goo.gl/o5DCG5
MICC laboratories, Florence, 21st October 2009 (10.30-13.30). Course on Multimedia Databases (DBMM) – laboratory lecture.
- Goal: logo recognition in web images.
- Dataset/testset: find 2 different logos vs 100 images.
- Evaluation metrics: recognition performances will be evaluated in terms of Precision and Recall.
Tutors: Lamberto Ballan, Lorenzo Seidenari.
Download slides 
(with references) | Download Software & Dataset
Comments Off on ACM MM’15 Tutorial on Image Tag Assignment, Refinement and Retrieval 
We gave a tutorial on “Image Tag Assignment, Refinement and Retrieval” at ACM MM 2015, based on our recent survey. Our tutorial focuses on challenges and solutions for content-based image retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems: tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, and recognize their merits and limitations.
Themocracy