4,435 research outputs found
Scene Classification with a Biologically Inspired Method
We present a biologically motivated method for scene image classification. The core of the method is to use shape based image property that is provided by a hierarchical feedforward model of the visual cortex [18]. Edge based and color based image properties are additionally used to improve the accuracy. The method consists of two stages of image analysis. In the first stage, each of three paths of classification uses each image property (i.e. shape, edge or color based features) independently. In the second stage, a single classifier assigns the category of an image based on the probability distributions of the first stage classifier outputs. Experiments show that the method boosts the classification accuracy over the shape based model. We demonstrate that this method achieves a high accuracy comparable to other reported methods on publicly available color image dataset
Automatic object classification for surveillance videos.
PhDThe recent popularity of surveillance video systems, specially located in urban
scenarios, demands the development of visual techniques for monitoring purposes.
A primary step towards intelligent surveillance video systems consists on automatic
object classification, which still remains an open research problem and the keystone
for the development of more specific applications.
Typically, object representation is based on the inherent visual features. However,
psychological studies have demonstrated that human beings can routinely categorise
objects according to their behaviour. The existing gap in the understanding
between the features automatically extracted by a computer, such as appearance-based
features, and the concepts unconsciously perceived by human beings but
unattainable for machines, or the behaviour features, is most commonly known
as semantic gap. Consequently, this thesis proposes to narrow the semantic gap
and bring together machine and human understanding towards object classification.
Thus, a Surveillance Media Management is proposed to automatically detect and
classify objects by analysing the physical properties inherent in their appearance
(machine understanding) and the behaviour patterns which require a higher level of
understanding (human understanding). Finally, a probabilistic multimodal fusion
algorithm bridges the gap performing an automatic classification considering both
machine and human understanding.
The performance of the proposed Surveillance Media Management framework
has been thoroughly evaluated on outdoor surveillance datasets. The experiments
conducted demonstrated that the combination of machine and human understanding
substantially enhanced the object classification performance. Finally, the inclusion
of human reasoning and understanding provides the essential information to bridge
the semantic gap towards smart surveillance video systems
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Biologically-inspired robust motion segmentation using mutual information
This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance
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