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Discriminative models for robust image classification

arXiv.org Machine Learning

A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant challenge. This dissertation explores the development of discriminative models for robust image classification that exploit underlying signal structure, via probabilistic graphical models and sparse signal representations. Probabilistic graphical models are widely used in many applications to approximate high-dimensional data in a reduced complexity set-up. Learning graphical structures to approximate probability distributions is an area of active research. Recent work has focused on learning graphs in a discriminative manner with the goal of minimizing classification error. In the first part of the dissertation, we develop a discriminative learning framework that exploits the complementary yet correlated information offered by multiple representations (or projections) of a given signal/image. Specifically, we propose a discriminative tree-based scheme for feature fusion by explicitly learning the conditional correlations among such multiple projections in an iterative manner. Experiments reveal the robustness of the resulting graphical model classifier to training insufficiency.


Multimodal User Supervised Interface and Intelligent Control (MUSHC) Matthew Beitler, Zunaid Kazi, Marcos Salganicoff, Richard Foulds, Shoupu Chen and Daniel Chester

AAAI Conferences

This research involves a method and system which integrates multimodal human-computer interaction with reactive planning to operate a telerobot for use as an assistive device. The Multimodal User Supervised Interface and Intelligent Control (MUSIIC) strategy is a novel approach for intelligent assistive telerobotic system. This approach to robotic interaction is both a step towards addressing the problem of allowing individuals with physical disabilities to operate a robot in an unstructured environment and an illustration of general principles of integrating speech-deictic gesture control with a knowledge-driven reactive planner and a stereo-vision system. Introduction While interfacing and control are areas of rehabilitation robotics which have been significantly researched [Foulds, 1986; Gilbert and Trefsger, 1990], unfortunately none of the resulting prototypes have met the requirements of the user community. To achieve effective use by individuals with disabilities, the prototypes have taken two interface approaches: command-based and control-based. In command-based interfaces, the robot is programmed with predefined movements and it is expected that items which the robot is manipulating will be in predetermined locations [Seamone and Schmeisser, 1986; Fu, 1986; Gilbert and Foulds, 1987; Van der loos et al., 1990; Van der loos et al., 1991; Hammel, Van der loose, Perkash, 1991; Beitler, Stanger, Howell, 1994], which limits the use to a preset workstation type environment.


Lego Finds An Inventive Way to Combine AI and Motion Tracking

#artificialintelligence

Lego toy systems have been around for generations and have been considered by many as a way to stimulate the imagination. Quite a few users have at some point imagined having a Lego figure in their own image they could use with their sets. Realizing that fact, Lego has decided to try and make that dream come true. As Gizmodo reports, Lego will try to realize that dream for anybody who visits there theme park that will open in New York in 2020. To do this the company will employ sophisticated motion tracking and neural network facial recognition.


Analysis Dictionary Learning: An Efficient and Discriminative Solution

arXiv.org Machine Learning

Yang et al. [7] used Fisher widely advocated for image classification problems. To further Information criterion in their class-specific reconstruction errors sharpen their discriminative capabilities, most state-ofthe-art to compose their approach. DL methods have additional constraints included in Besides SDL, Analysis Dictionary Learning (ADL) [8, 9] the learning stages. These various constraints, however, lead has recently been of interest on account of its fast encoding to additional computational complexity. We hence propose an and stability attributes. ADL provides a linear transformation efficient Discriminative Convolutional Analysis Dictionary of a signal to a nearly sparse representation. Inspired by Learning (DCADL) method, as a lower cost Discriminative the SDL methodology in image classification, ADL has also DL framework, to both characterize the image structures and been adapted to the supervised learning problems by promoting refine the interclass structure representations. The proposed discriminative sparse representations [10, 11]. In [10], DCADL jointly learns a convolutional analysis dictionary and Guo et al. incorporated both a topological structure and a representation a universal classifier, while greatly reducing the time complexity similarity constraint to encourage a suitable classselective in both training and testing phases, and achieving a representation for a 1-Nearest Neighbor classifier.


AI system to diagnose pain levels in sheep

#artificialintelligence

Building on earlier work which teaches computers to recognise emotions and expressions in human faces, the system is able to detect the distinct parts of a sheep's face and compare it with a standardised measurement tool developed by veterinarians for diagnosing pain. Their results will be presented today (1 June) at the 12th IEEE International Conference on Automatic Face and Gesture Recognition in Washington, DC. Severe pain in sheep is associated with conditions such as foot rot, an extremely painful and contagious condition which causes the foot to rot away; or mastitis, an inflammation of the udder in ewes caused by injury or bacterial infection. Both of these conditions are common in large flocks, and early detection will lead to faster treatment and pain relief. Reliable and efficient pain assessment would also help with early diagnosis.