Instance Retrieval Using Region of Interest Based CNN Features

Region-Based Image Retrieval Using Multiple-Features

Content-based image retrieval from large multimedia databases effectively and efficiently is a challenging task. In this paper, we propose a retrieval technique that utilizes the regional properties of the images. After image segmentation, each region is represented by its colour, shape, size, and spatial position. Regions of different images are matched and a distance measure between the whole...

Region-Based Image Clustering and Retrieval Using Multiple Instance Learning

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Alg...

Image Retrieval Based on Region of Interest

Content Based Image Retrieval (CBIR) is browsing, searching and navigation of images from large image databases based on their visual content. CBIR has been an active area of research for more than a decade. Many CBIR systems have been developed; like QBIC [Flickner et al. (1995)], Simplicity [Wang et al. (2001)], and Blob world [Carson et al.(2002)]. A detailed survey of CBIR techniques can be...

Content Based Image Retrieval Using Interest Points and Texture Features

Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. In this work we present methods for content based image retrieval based on texture similarity using interest points and Gabor features. Interest point detectors are used in computer vision to detect image points with special properties, which can be geometric (corn...

Content Based Image Retrieval Using Interest Points and Texture Features