Fwd: Computer Vision Seminar at Sharif On Monday

[vahid asaeedi]

---------- Forwarded message ----------
From: vahid asaeedi <vahid....@gmail.com>
Date: Sun, May 18, 2014 at 12:25 PM
Subject: Fwd: Computer Vision Seminar at Sharif On Monday
To: Ali Akramizadeh <akram...@gmail.com>

---------- Forwarded message ----------
From: Soraya Panahi <sor...@gmail.com>
Date: Sun, May 18, 2014 at 8:37 AM
Subject: Computer Vision Seminar at Sharif On Monday
To:
Cc: Mehrdad Mirshams Shahshahani <mshahs...@gmail.com>, Mostafa Kamali <most...@gmail.com>

Hello everybody,

Mohammad Rastegari from university of Maryland will give a lecture about his current research on  "Rich and Efficient Visual Data Representation".

The seminar will be held at 9 on Monday (Ordibehesht 29) at room 19 Ebne Sina Building.

You can find the abstract of this lecture in the following.

Bests,

Soraya Panahi

ABSTRACT:      
 Increasing the size of training data in many computer
vision tasks has shown to be very effective. Using large scale image
datasets (e.g. ImageNet) with simple learning techniques (e.g. linear
classifiers) one can achieve state-of-the-art performance in object
recognition compare to sophisticated learning technique on smaller
image sets. Semantic search on visual data become very popular. There
are billions of images on the internet which are increasing every day.
Dealing with large scale image sets is intense per se. They take heavy
amount of memory that makes it impossible to process the images with
complex algorithms on single CPU machines. Finding an efficient image
representation can be a key to detract this problem. A representation
to be efficient is not enough for image understanding. It should be
comprehensive and rich in carrying semantic information. In this
proposal we show binary codes as a rich and efficient image
representation. We demonstrate several tasks in which binary features
can be very effective. We show how binary features can speed up the
large scale image classification. We present learning techniques to
learn the binary features from supervised image set (With different
ways of semantic supervision; class labels, textual descriptions). We
propose several problems that are very important in finding and using
efficient image representation.