IEEE 2015 MATLAB MODELING NEURON SELECTIVITY OVER SIMPLE MIDLEVEL FEATURES FOR.pptx

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MODELING NEURON SELECTIVITY OVER SIMPLE MIDLEVEL
FEATURES FOR IMAGE CLASSIFICATION
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ABSTRACT

• Our proposed that good mid-level features can
  greatly enhance the performance of image
  classification, but how to efficiently learn the
  image features is still an open question. In this
  paper, we present an efficient unsupervised
  midlevel feature learning approach (MidFea),
  which only involves simple operations, such as
  k-means clustering, convolution, pooling, vector
  quantization, and random projection. We show this
  simple feature can also achieve good performance
  in traditional classification task. To further
  boost the performance, we model the neuron
  selectivity (NS) principle by building an
  additional layer over the midlevel features prior
  to the classifier. The NS-layer learns category
  specific neurons in a supervised manner with both
  bottom-up inference and top-down analysis,

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• and thus supports fast inference for a query
  image. Through extensive experiments, we
  demonstrate that this higher level NS-layer
  notably improves the classification accuracy with
  our simple MidFea, achieving comparable
  performances for face recognition, gender
  classification, age estimation, and object
  categorization. In particular, our approach runs
  faster in inference by an order of magnitude than
  sparse coding-based feature learning methods. As
  a conclusion, we argue that not only do carefully
  learned features (MidFea) bring improved
  performance, but also a sophisticated mechanism
  (NS-layer) at higher level boosts the performance
  further.

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EXISTING SYSTEM

• The existing methods usually make use of a
  hierarchical architecture in which each layer
  accumulates information from the layer beneath to
  form more complex features. The nonlinearity in
  our approach is reflected by the convolution with
  thresholding, max-pooling and VQ. Although these
  operations are adopted by existing methods ours
  is a purely feed-forward one that runs faster and
  does not introduce heavily parameterized
  functions as opposed to PSD and CDBN. Through
  comparison with SIFT and HMAX, we explain why our
  MidFea learns desirable features.

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Proposed system

• Our propose a simple and efficient method to
  learn mid-level features, and give the
  explanation why the proposed approach generates
  desirable features. We model the neuron
  selectivity principle over the mid-level features
  by the NS-layer to boost the performance. The
  NS-layer is a general layer that supports both
  top-down analysis and bottom-up inference, which
  is an appealing property in real-world
  application. Through extensive experiments, we
  demonstrate our framework not only achieves
  comparable or even state-of-the-art results on
  public databases, but also runs faster by an
  order of magnitude than the related sparse coding
  based methods.

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SOFTWARE REQUIREMENTS

• Mat Lab R 2015a
• Image Processing Toolbox 7.1