Sorry for spamming but the title of the email might have been a bit confusing.

To emphasize:  Tea talk is **Thursday 13th Feb**.

Razvan



On Tue, Feb 11, 2014 at 8:28 AM, Razvan Pascanu <r.pascanu@gmail.com> wrote:
Hi all,

Thursday we have a tea talk by Jyri Kivinen. The room is going to be Z300 McNicoll pavilion, time 14h00 to 15h00. PLEASE NOTE THE CHANGE OF TIME AND PLACE.

Here is the title and abstract:

Title:
Statistical Models for Natural Scene Data

Abstract:
This talk describes work largely described in my PhD thesis (University of Edinburgh, 2013) titled Statistical Models for Natural Scene Data.

We consider statistical modelling of natural image data. Obtaining advances in this field can have significant impact for both engineering applications, and for the understanding of the human visual system. Several recent advances in natural image modeling have been obtained with the use of unsupervised feature learning. We consider a class of such models, restricted Boltzmann machines (RBMs), used in many recent state-of-the-art image models. We develop extensions of these stochastic artificial neural networks, and use them as a basis for building more effective image models, and tools for computational vision.

First, motivated by the unsatisfactory results of current generative natural image models, we take a step back, and evaluate whether they are able to model a subclass of the data, natural image textures. This is a necessary subcomponent of any credible model for visual scenes. We assess the performance of a state-of-the-art model of natural images for texture generation, using a dataset and evaluation techniques from in prior work. We also perform a dissection of the model architecture, uncovering the properties important for good performance.  Building on this, we develop structured extensions for more complicated data comprised of textures from multiple classes, using the single-texture
model architecture as a basis. These models are shown to be able to produce state-of-the-art texture synthesis results quantitatively, and are also effective qualitatively. It is demonstrated empirically that the developed multiple-texture framework provides a means to generate images of differently textured regions, more generic globally varying textures, and can also be used for texture interpolation, where the approach is radically different from the others in the area.

We then consider visual boundary prediction from natural images. The work aims to improve understanding of Boltzmann machines in the generation of image segment boundaries, and to investigate deep neural network architectures for learning the boundary detection problem. The developed networks (which avoid several hand-crafted model and feature designs commonly used for the problem), produce effective inference with state-of-the-art or better performance.

Finally we discuss a novel framework we have developed for obtaining Boltzmann machines, in which the hidden unit activations co-transform with transformed input stimuli in a stable and predictable way throughout the network. We define such models to be \emph{transformation equivariant}. Such properties have been shown useful for computer vision systems, and have been motivational for example in the development of steerable filters, a widely used classical feature extraction technique. Translation equivariant feature sharing has been the standard method for scaling image models beyond patch-sized data to large images. In our framework we extend shallow and deep models to account for other kinds of transformations as well, focusing on in-plane rotations.

I hope to see many of you there !

Best,
Razvan