30-07-2014, 03:28 PM
A NOVEL CONTENT BASED IMAGE RETRIEVAL MODEL BASED ON THE
MOST RELEVANT FEATURES USING PARTICLE SWARM OPTIMIZATION
A NOVEL CONTENT.pdf (Size: 246.09 KB / Downloads: 27)
Abstract:
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.
INTRODUCTION
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. CBIR systems retrieve relevant images in a
database using visual content of the images. Researchers are
mainly developed based on the high-level semantic analysis
of the image content along with the visual content of the
image such as colors, textures, and shapes. Color features
include the color histogram, the color coherence vector, the
color co-occurrence matrix, vector quantization, and color
moments. Texture features are derived from the gray-level
co-occurrence matrix, the Tamura feature, and wavelet
coefficients, and Gabor filter-based features. Once image
features are extracted, another problem arouse that is which
features are relevant in the retrieval proce