- author = "Mengjie Zhang and Victor Ciesielski",
- title = "Neural Networks and Genetic Algorithms for Domain Independent Multiclass Object Detection",
- journal = "International Journal of Computational Intelligence and Applications",
- year = "2004",
- volume = "4",
- number = "1",
- pages = "77--108",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Network training, network refinement, network sweeping, evolutionary process, domain independent, object recognition, target recognition, target detection",
- ISSN = "1469-0268",
-
DOI = "
doi:10.1142/S146902680400115X",
-
abstract = "This paper describes a domain independent approach to
multiple class rotation invariant 2D object detection
problems. The approach avoids preprocessing,
segmentation and specific feature extraction. Instead,
raw image pixel values are used as inputs to the
learning systems. Five object detection methods have
been developed and tested, the basic method and four
variations which are expected to improve the accuracy
of the basic method. In the basic method cutouts of the
objects of interest are used to train multilayer feed
forward networks using back propagation. The trained
network is then used as a template to sweep the full
image and find the objects of interest. The variations
are (1) Use of a centred weight initialisation method
in network training, (2) Use of a genetic algorithm to
train the network, (3) Use of a genetic algorithm, with
fitness based on detection rate and false alarm rate,
to refine the weights found in basic approach, and (4)
Use of the same genetic algorithm to refine the weights
found by method 2. These methods have been tested on
three detection problems of increasing difficulty: an
easy database of circles and squares, a medium
difficulty database of coins and a very difficult
database of retinal pathologies. For detecting the
objects in all classes of interest in the easy and the
medium difficulty problems, a 100percent detection rate
with no false alarms was achieved. However the results
on the retinal pathologies were unsatisfactory. The
centred weight initialization algorithm improved the
detection performance over the basic approach on all
three databases. In addition, refinement of weights
with a genetic algorithm significantly improved
detection performance on the three databases.
The goal of domain independent object recognition was achieved for the detection of relatively small regular objects in larger images with relatively uncluttered backgrounds. Detection performance on irregular objects in complex, highly cluttered backgrounds such as the retina pictures, however, has not been achieved to an acceptable level.",
- notes = "http://ejournals.wspc.com.sg/ijcia/ijcia.shtml",
