Computer Science: Recent submissions

  • A one-pass resource-allocating codebook for patch-based visual object recognition 
    Ramanan, A.; Niranjan, M. (IEEE, 2010)
    Frequencies of occurrence of low-level image features is the representation of choice in the design of state-of-the-art visual object recognition systems. A crucial step in this process is the construction of a codebook ...
  • Robust invariant descriptors for visual object recognition 
    Barathy, G.; Mahesan, S.; Pinidiyaarachchi, U.A.J. (IEEE, 2011)
    In the state-of-the-art visual object recognition, there are a number of descriptors that have been proposed for various visual recognition tasks. But it is still difficult to decide which descriptors have more significant ...
  • Sequential hierarchical pattern clustering 
    Farran, B.; Ramanan, A.; Niranjan, M. (Springer Berlin Heidelberg, 2009-09-07)
    Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed ...
  • Control chart pattern clustering using a new self-organizing spiking neural network 
    Pham, D.T.; Packianather, M.S.; Charles, E.Y.A. (IMechE, 2008)
    This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking ...
  • A self-organising spiking neural network trained using delay adaptation 
    Pham, D.T.; Packianather, M.S.; Charles, E.Y.A. (IEEE, 2007-06)
    This paper proposes a self-organising delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection ...
  • A Novel Self-Organised Learning Model with Temporal Coding for Spiking Neural Networks 
    Pham, D.T.; Packianather, M.S.; Charles, E.Y.A. (Elsevier Ltd, 2006-07)
    This chapter proposes a novel self-organized learning model with temporal coding for a network of spiking neurons, which encode information through the timing of action potentials. The development of this learning model ...
  • Resource-allocating codebook for patch-based face recognition 
    Ramanan, A.; Niranjan, M. (IEEE, 2009-12)
    In this paper we propose a novel approach to constructing a discriminant visual codebook in a simple and extremely fast way as a one-pass, that we call Resource-Allocating Codebook (RAC), inspired by the Resource Allocating ...
  • Speeding up multi-class texture classification by one-pass vocabulary design and decision tree 
    Ramanan, A.; Ranganathan, P.; Niranjan, M. (IEEE, 2011-08)
    The bag-of-keypoints representation started to be used as a black box providing reliable and repeatable measurements from images for a wide range of applications such as visual object recognition and texture classification. ...
  • Unbalanced decision trees for multi-class classification 
    Ramanan, A.; Suppharangsan, S.; Niranjan, M. (IEEE, 2007-08)
    In this paper we propose a new learning architecture that we call Unbalanced Decision Tree (UDT), attempting to improve existing methods based on Directed Acyclic Graph (DAG) [1] and One-versus-All (OVA) [2] approaches to ...
  • Self similarity and predictability of contact opportunities in opportunistic networks 
    Thabotharan, Kathiravelu; Ranasinghe, N (IEEE, 2012-12)
    Predicting future contact opportunities in opportunistic networks can assist mobile nodes to make intelligent decisions on efficient content forwarding and can greatly improve the message delivery ratio. But predicting ...
  • A hybrid reactive routing protocol for Mobile Ad-hoc Networks 
    Kathiravelu, T.; Sivasuthan, S. (IEEE, 2011-08)
    A lot of research effort has been put by researchers in developing routing protocols for Mobile Ad-hoc Networks (MANETs). Each protocol proposed and designed so far has its own merits and demerits. Researchers around the ...