Revision as of 01:00, 31 January 2013 by Ejaz (talk | contribs)

Computer Vision Student Seminars

The Computer Vision Student Seminars at the University of Maryland College Park are a student-run series of talks given by current graduate students for current graduate students.

To receive regular information about the Computer Vision Student Seminars, subscribe to our mailing list or our talks list.

Description

The purpose of these talks is to:

  • Encourage interaction between computer vision students;
  • Provide an opportunity for computer vision students to be aware of and possibly get involved in the research their peers are conducting;
  • Provide an opportunity for computer vision students to receive feedback on their current research;
  • Provide speaking opportunities for computer vision students.

The guidelines for the format are:

  • An hour-long weekly meeting, consisting of one 20-40 minute talk followed by discussion and food.
  • The talks are meant to be casual and discussion is encouraged.
  • Topics may include current research, past research, general topic presentations, paper summaries and critiques, or anything else beneficial to the computer vision graduate student community.


Schedule Spring 2013

All talks take place Thursdays at 4:30pm in AVW 3450.

Date Speaker Title
January 24 (no meeting)
January 31 Mohammad Rastegari Scalable object-class retrieval with approximate and top-k ranking
February 7
February 14
February 21
February 28 Kota Hara
March 7 Arijit Biswas
March 14
March 21 (Spring Break, no meeting)
March 28 (Midterms, no meeting)
April 4
April 11 (ICCV deadline, no meeting)
April 18 Raviteja Vemulapalli
April 25
May 2 Xavier Gibert Serra

Talk Abstracts Spring 2013

Scalable object-class retrieval with approximate and top-k ranking

Speaker: Mohammad Rastegari -- Date: January 31, 2013

In this paper we address the problem of object-class retrieval in large image data sets: given a small set of training examples defining a visual category, the objective is to efficiently retrieve images of the same class from a large database. We propose two contrasting retrieval schemes achieving good accuracy and high efficiency. The first exploits sparse classification models expressed as linear combinations of a small number of features. These sparse models can be efficiently evaluated using inverted file indexing. Furthermore, we introduce a novel ranking procedure that provides a significant speedup over inverted file indexing when the goal is restricted to finding the top-k (i.e., the k highest ranked) images in the data set. We contrast these sparse retrieval models with a second scheme based on approximate ranking using vector quantization. Experimental results show that our algorithms for object-class retrieval can search a 10 million database in just a couple of seconds and produce categorization accuracy comparable to the best known class-recognition systems.

Past Semesters

Funded By

  • Computer Vision Faculty
  • Northrop Grumman

Current Seminar Series Coordinators

Emails are at umiacs.umd.edu.

Ejaz Ahmed, ejaz@ (student of Professor Larry Davis)
Angjoo Kanazawa, kanazawa@ (student of Professor David Jacobs)
Jie Ni, jni@ (student of Professor Rama Chellappa)
Ching Lik Teo, cteo@ (student of Professor Yiannis Aloimonos)

Gone but not forgotten.

Anne Jorstad now PostDoc at EPFL
Sameh Khamis off this semester
Sima Taheri