Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

Text mining with the WEBSOM

The emerging field of text mining applies methods from data mining and exploratory data analysis to analyzing text collections and to conveying information to the user in an intuitive manner. Visual, map-like displays provide a powerful and fast medium for portraying information about large collections of text. Relationships between text items and collections, such as similarity, clusters, gaps and outliers can be communicated naturally using spatial relationships, shading, and colors. In the WEBSOM method the self-organizing map (SOM) algorithm is used to automatically organize very large and high-dimensional collections of text documents onto two-dimensional map displays. The map forms a document landscape where similar documents appear close to each other at points of the regular map grid. The landscape can be labeled with automatically identified descriptive words that convey properties of each area and also act as landmarks during exploration. With the help of an HTML-based interactive tool the ordered landscape can be used in browsing the document collection and in performing searches on the map. An organized map offers an overview of an unknown document collection helping the user in familiarizing herself with the domain. Map displays that are already familiar can be used as visual frames of reference for conveying properties of unknown text items. Static, thematically arranged document landscapes provide meaningful backgrounds for dynamic visualizations of for example time-related properties of the data. Search results can be visualized in the context of related documents. Experiments on document collections of various sizes, text types, and languages show that the WEBSOM method is scalable and generally applicable. Preliminary results in a text retrieval experiment indicate that even when the additional value provided by the visualization is disregarded the document maps perform at least comparably with more conventional retrieval methods.reviewe

Human Face Detection: Manual vs. Kohonen Self Organizing Map

In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbor Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method

Vowel recognition using Kohonen\u27s self-organizing feature maps

An important organizing principle observed in the sensory pathways in the brain is the orderly placement of neurons. Although the neurons are structurally identical, the specialized role played by each unit is determined by its internal parameters that are made to change during early learning processes. In the human auditory system, the nerve cells and fibres are arranged in a manner that would elicit maximum response from the neurons when they are activated. Although most of this organization is genetically determined, some of the high level organization is created due to algorithms that promote self-organization. Kohonen\u27s self-organizing feature map is a neural net model that produces feature maps similar to the ones produced in the brain. These maps are capable of describing topological relationships of input signals using a one or two dimensional representation. This technique uses unlabeled data and requires no training as in supervised learning algorithms. It is hence immensely useful in speech and vision applications. This neutral net has been implemented for the recognition of vowels in the American English language. The net has been trained and tested with vowel data. The formation of internal clusters or categories has been observed and closely reflects the tonotopic relationships between the vowels. An analysis of the results has been carried out and the performance has been compared to other classification techniques. A graphical user interface has also been developed using Xview to help visualize the formation of the maps during the training and testing processes