Greedy Learning of Multiple Objects in Images using Robust Statistics and Factorial Learning

We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images

Unsupervised Learning of Multiple Objects in Images

Institute for Adaptive and Neural ComputationDeveloping computer vision algorithms able to learn from unsegmented images containing multiple objects is important since this is how humans constantly learn from visual experiences. In this thesis we consider images containing views of multiple objects and our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image is explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a greedy algorithm to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion. When we have video data, we greatly speed up the greedy algorithm by carrying out approximate tracking of the multiple objects in the scene. This method is applied to raw image sequence data and extracts the objects one at a time. First, the (possibly moving) background is learned, and moving objects are found at later stages. The algorithm recursively updates an appearance model so that occlusion is taken into account, and matches this model to the frames through the sequence. We apply this method to learn multiple objects in image sequences as well as articulated parts of the human body. Additionally, we learn a distribution over parts undergoing full affine transformations that expresses the relative movements of the parts. The idea of fitting a model to data sequentially using robust statistics is quite general and it can be applied to other models. We describe a method for training mixture models by learning one component at a time and thus building the mixture model in a sequential manner. We do this by incorporating an outlier component into the mixture model which allows us to fit just one data cluster by "ignoring" the rest of the clusters. Once a model is fitted we remove from consideration all the data explained by this model and then repeat the operation. This algorithm can be used to provide a sensible initialization of the mixture components when we train a mixture model

An Efficient Learning Procedure for Deep Boltzmann Machines

We present a new learning algorithm for Boltzmann Machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann Machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer "pre-training" phase that initializes the weights sensibly. The pre-training also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB datasets showing that Deep Boltzmann Machines learn very good generative models of hand-written digits and 3-D objects. We also show that the features discovered by Deep Boltzmann Machines are a very effective way to initialize the hidden layers of feed-forward neural nets which are then discriminatively fine-tuned

Autonomous Cleaning of Corrupted Scanned Documents - A Generative Modeling Approach

We study the task of cleaning scanned text documents that are strongly corrupted by dirt such as manual line strokes, spilled ink etc. We aim at autonomously removing dirt from a single letter-size page based only on the information the page contains. Our approach, therefore, has to learn character representations without supervision and requires a mechanism to distinguish learned representations from irregular patterns. To learn character representations, we use a probabilistic generative model parameterizing pattern features, feature variances, the features' planar arrangements, and pattern frequencies. The latent variables of the model describe pattern class, pattern position, and the presence or absence of individual pattern features. The model parameters are optimized using a novel variational EM approximation. After learning, the parameters represent, independently of their absolute position, planar feature arrangements and their variances. A quality measure defined based on the learned representation then allows for an autonomous discrimination between regular character patterns and the irregular patterns making up the dirt. The irregular patterns can thus be removed to clean the document. For a full Latin alphabet we found that a single page does not contain sufficiently many character examples. However, even if heavily corrupted by dirt, we show that a page containing a lower number of character types can efficiently and autonomously be cleaned solely based on the structural regularity of the characters it contains. In different examples using characters from different alphabets, we demonstrate generality of the approach and discuss its implications for future developments.Comment: oral presentation and Google Student Travel Award; IEEE conference on Computer Vision and Pattern Recognition 201