swarm intelligence: where biology meets computers · swarm intelligence: where biology meets...
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Swarm Intelligence: Where Biology meets Computers
Tomas Piatrik
Multimedia and Vision Research Group
Queen Mary University of London
Multimedia and Vision Research Group
Overview
• Nature and Biology in Computer Science
• Popular Biological Inspired Systems
• Swarm Intelligence
– Ant Colony Optimisation
• Implementation of ACO for Clustering of Visual Data
• Conclusions
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Nature and Biology in Computing
• The Power of Nature – Connectionism
– Social Behaviour
– Emergence
– Adaptation
• Systems inspired by nature: – Evolutionary algorithms -- River formation dynamics --
Intelligent Water Drops -- Gravitational Search -- Stochastic Diffusion Search -- Particle Swarm Optimisation -- Ant Colony Optimisation -- Artificial Neural Networks -- Artificial Immune Systems . . . etc.
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Popular Biologically Inspired Systems
• Genetic algorithms – inheritance, – mutation, – natural selection, – genetic crossover
• Artificial Neural Networks
– neurons – connections – weights – local processing, distributed memory, synaptic weight dynamics and
weight modification by experience
• Artificial Immune Systems – learning and memory – clonal selection, negative selection, immune networks
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Swarm Intelligence
• What is it?
Characteristics:
• simple agents
• little knowledge
• decentralised control
• self organisation
• global behaviour
• able to solve complex problems
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Ant Colony Optimisation
• Meta-Heuristic
• Pheromone
• Shortest Path
• The probability of choosing direction:
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Image Clustering using ACO
• The probability of clustering image i to cluster j:
• Updating pheromone level for all images:
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Ant Tree Strategy for Visual Classification
• Inspired by self-assembling behavior of African ants and their ability to build chains (bridges) by their bodies in order to link leaves together.
• We model the ability of ants to build live structures with their bodies in order to discover, in a distributed and unsupervised way, a tree-structured organization of the visual data.
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• General principles
each ant: represents node of tree (data)
- ao support, apos position of moving ant
- incoming links; other ants maintain
toward ant i
Ant-Tree Strategy
• Main algorithm 1. all ants placed on the support;
initialization: Tsim(i)=1, Tdissim(i)=0
2. While there exists non connected ant i Do
3. If ant i is located on the support Then Support case
4. Else Ant case
5. End While
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Proposed ACO for Feature Selection
Lion Building Rural Car Elephant Clouds
CSD, CLD
CLD, EHD TGF, EHD
CSD, TGF
CSD, DCD, GLC CSD, CLD
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• The Corel image database - 600 images with 6 semantic concepts
• The Window on the UK 2000” database - 390 images with 6 sets
Experimental Evaluation
Lions Rural Buildings Cars Elephants Clouds
Boats Fields Vehicles Trees Buildings Roads
• The Caltech Image Database - 3550 images, 40 semantic concepts
Watch Water Lilly Wild cat Scissors Yin Yang
Ant Pizza Face Dollar Bill Snoopy
Soccer ball Stop Sign Strawberry Sunflower Umbrella
Experimental Evaluation
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• Flickr Image Database - 500 images segmented into regions, 4320 regions
• Semantic Concepts: Sand, Sea, Vegetation, Building, Sky, Person, Rock, Tree, Grass, Ground, and.
Experimental Evaluation
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Experimental Evaluation
Original Camera video: 5min. Video summary: 53 sec.
• We cluster video frames according to similarity of the content. • From each cluster, one video segment is taken as a part of the summary
Set of representative frames for each scene:
Ant-tree algorithm
• Inspired by self-assembling behavior of African ants.
• We model the ability of ants to build live structures with their bodies in order to discover, in a distributed and unsupervised way, a tree-structured organization and summarization of the video data.
Frames are placed to the tree according to visual similarity and temporal information
Video Summarisation using Ants
Conclusion
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• Nature and Biology – Source of inspiration for scientists
• Importance of building artifacts whose inspiration derives from biology and the natural world
THANK YOU