A NOVEL CONTENT BASED IMAGE RETRIEVAL MODEL BASED ON THE MOST RELEVANT FEATURES USING PARTICLE SWARM OPTIMIZATION
Content Based Image Retrieval (CBIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval (CBIR) depends on extracting the most relevant features according to a feature selection technique. The integration of multiple features may cause the curse of dimensionality and the consumed time in the retrieval process. The proposed model includes the following steps: (i) Feature Extraction from images database using color coherence vector (CCV) and Gabor filter algorithm to extract the color and texture features (ii) Feature Discrimination using maximum entropy method for replacing numerical features with nominal features that represent intervals of numerical domains with discrete values using Class Attribute Interdependence Maximization (CAIM) algorithm (iii) Feature Selection using Particle Swarm Optimization (PSO) algorithm for extracting the most relevant features from the original features set. CBIR based applications are used in Internet and law enforcement markets for the purpose of identifying and censoring the images.
P.K.Bhargavi, S.Bhuvana, Dr.R.Radhakrishnan
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