ABSTRACT
Content Based Image Retrieval is an important technique which uses visual contents to retrieve images from large database. Many traditional methods have been employed to retrieve images. Relevance feedback is often a critical component when designing image databases. Relevance feedback interactively determines the user’s query by asking the user whether image is relevant or not. The use of support vector machine active learning algorithm makes this task more easy and effective. This algorithm selects the most informative images that satisfies the user’s requirement. Experiment results show that this algorithm achieves the effective results. Content based image retrieval(CBIR), also known as query by image content(QBIC)and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem. “Content-based” means that the search will analyze the actual contents of the image. The term ‘content’ in this context might refers color, shapes, textures or any other information like similarity matrix which compare pixel by pixel value that can be derived from the image itself. One key design task, when constructing image databases, is the creation of an effective relevance feedback components. Creating the database by relevance feedback or by hand labeling each image is time consuming, costly and subjective.
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Contact:
Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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