Hybrid Approach For Effective Feature Extraction Technique in Content Based Image Retrieval

Image Processing

Content Based Image Retrieval (CBIR) is a very important and increasingly popular approach that helps in the retrieval of image data from a huge collection of Image. Image representation based on certain features helps in retrieval process. Content-based image retrieval technique is in huge demand for few domains like Weather forecasting, data mining, remote sensing, medical imaging, education, crime prevention and management of earth resources. Content Based Image Retrieval (CBIR) is process to find similar image data in the large amount of image database when a query image is given by user. A user gives input to the system in the form of specified query image and system return set of relevant images related to query image. In this report we analyze different Content Based Image Retrieval techniques and their comparative study. Image retrieval process and improve visual similarity search in content-based image retrieval many studies have been conducted and many methods developed in recent years, but there are a few issues that need to be addressed. Color, shape and texture feature are extracted from the image. To improve accuracy in terms of Precision and Recall, Ant colony Optimization (ACO) algorithm and Support Vector Machine (SVM) method is used in proposed model.

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