Learning Discriminative Feature Representations for Visual Categorization

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{thesis_liuli,
  author =       "Li Liu",
  title =        "Learning Discriminative Feature Representations for
                 Visual Categorization",
  school =       "Electronic and Electrical Engineering, The University
                 of Sheffield",
  year =         "2015",
  address =      "UK",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://etheses.whiterose.ac.uk/8239/1/thesis_liuli.pdf",
  URL =          "http://etheses.whiterose.ac.uk/8239/",
  size =         "180 pages",
  abstract =     "Learning discriminative feature representations has
                 attracted a great deal of attention due to its
                 potential value and wide usage in a variety of areas,
                 such as image/video recognition and retrieval, human
                 activities analysis, intelligent surveillance and
                 human-computer interaction. In this thesis we first
                 introduce a new boosted key-frame selection scheme for
                 action recognition. Specifically, we propose to select
                 a subset of key poses for the representation of each
                 action via AdaBoost and a new classifier, namely
                 WLNBNN, is then developed for final classification. The
                 experimental results of the proposed method are
                 0.6percent - 13.2percent better than previous work.
                 After that, a domain-adaptive learning approach based
                 on multiobjective genetic programming (MOGP) has been
                 developed for image classification. In this method, a
                 set of primitive 2-D operators are randomly combined to
                 construct feature descriptors through the MOGP evolving
                 and then evaluated by two objective fitness criteria,
                 i.e., the classification error and the tree complexity.
                 Later, the (near-)optimal feature descriptor can be
                 obtained. The proposed approach can achieve 0.9percent
                 ∼ 25.9percent better performance compared with
                 state-of-the-art methods. Moreover, effective
                 dimensionality reduction algorithms have also been
                 widely used for obtaining better representations. In
                 this thesis, we have proposed a novel linear
                 unsupervised algorithm, termed Discriminative Partition
                 Sparsity Analysis (DPSA), explicitly considering
                 different probabilistic distributions that exist over
                 the data points, simultaneously preserving the natural
                 locality relationship among the data. All these above
                 methods have been systematically evaluated on several
                 public datasets, showing their accurate and robust
                 performance (0.44percent - 6.69percent better than the
                 previous) for action and image categorization.
                 Targeting efficient image classification , we also
                 introduce a novel unsupervised framework termed
                 evolutionary compact embedding (ECE) which can
                 automatically learn the task-specific binary hash
                 codes. It is regarded as an optimization algorithm
                 which combines the genetic programming (GP) and a
                 boosting trick. The experimental results manifest ECE
                 significantly outperform others by 1.58percent -
                 2.19percent for classification tasks. In addition, a
                 supervised framework, bilinear local feature hashing
                 (BLFH), has also been proposed to learn highly
                 discriminative binary codes on the local descriptors
                 for large-scale image similarity search. We address it
                 as a nonconvex optimization problem to seek orthogonal
                 projection matrices for hashing, which can successfully
                 preserve the pairwise similarity between different
                 local features and simultaneously take image-to-class
                 (I2C) distances into consideration. BLFH produces
                 outstanding results (0.017percent - 0.149percent
                 better) compared to the state-of-the-art hashing
                 techniques.",
}

Genetic Programming entries for Li Liu

Citations