The classification of general-purpose photographs into textured and non-textured images is critical for developing accurate content-based image retrieval systems for large-scale i...
Content-based image retrieval, which provides convenient ways to retrieve images from large image databases, has been studied actively. While many previous image retrieval techniq...
Jun-Wei Hsieh, W. Eric L. Grimson, Cheng-Chin Chia...
By using relevance feedback [6], Content-Based Image Retrieval (CBIR) allows the user to retrieve images interactively. The user can select the most relevant images and provide a ...
In this paper, a novel method of relevance feedback is presented based on Support Vector Machine learning in the content-based image retrieval system. A SVM classifier can be lear...
This work presents a novel approach to content-based image retrieval in categorical multimedia databases. The images are indexed using a combination of text and content descriptor...
In this paper, an optimal relevance algorithm is proposed, which adapts the response of a content-based image retrieval (CBIR) system to the user's information needs. In part...
The goal in image segmentation is to label pixels in an image based on the properties of each pixel and its surrounding region. Recently Content-Based Image Retrieval (CBIR) has e...
In this paper, an efficient method using various histogrambased (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categ...
Compressed domain image processing techniques are becoming increasingly important. Compressed domain retrieval It allows the calculation of image features and hence content-based ...