swarm intelligence: where biology meets computers · swarm intelligence: where biology meets...

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