A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization

Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the ¿semantic gap¿ problem. In this paper, an innovative ap...

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Vydané v:IEEE transactions on multimedia Ročník 12; číslo 4; s. 267 - 277
Hlavní autori: Broilo, Mattia, De Natale, Francesco G B
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.06.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Applied sciences
Artificial intelligence
Clustering algorithms
Computer science; control theory; systems
Computer simulation
Computer systems and distributed systems. User interface
Content-based image retrieval
Control theory. Systems
Exact sciences and technology
Feedback
Humans
Image converters
Image retrieval
Information retrieval
Iterative algorithms
Iterative methods
Mathematical models
Particle swarm optimization
particle swarm optimizer (PSO)
Pattern recognition. Digital image processing. Computational geometry
Radio frequency
relevance feedback (RF)
Retrieval
Robotics
Semantics
Software
Solution space
Stochastic processes
Stochasticity
Studies
Concept learning
Multimedia
Gripping
Content based retrieval
Probabilistic approach
Image retrieval
Evolutionary algorithm
particle swarm optimizer (PSO)
relevance feedback (RF)
Iterative method
Information retrieval
Neural network
Randomized algorithm
Radiofrequency
Particle swarm optimization
Relevance
Relevance feedback
Social interaction
Semantics
Graphical interface
Swarm intelligence
Content-based image retrieval
Deterministic approach
ISSN:1520-9210, 1941-0077
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Popis
Shrnutí:Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the ¿semantic gap¿ problem. In this paper, an innovative approach is proposed that combines a relevance feedback (RF) approach with an evolutionary stochastic algorithm, called particle swarm optimizer (PSO), as a way to grasp user's semantics through optimized iterative learning. The retrieval uses human interaction to achieve a twofold goal: 1) to guide the swarm particles in the exploration of the solution space towards the cluster of relevant images; 2) to dynamically modify the feature space by appropriately weighting the descriptive features according to the users' perception of relevance. Extensive simulations showed that the proposed technique outperforms traditional deterministic RF approaches of the same class, thanks to its stochastic nature, which allows a better exploration of complex, nonlinear, and highly-dimensional solution spaces.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2010.2046269