Myron D. Flickner

TL;DR: The Query by Image Content (QBIC) system as discussed by the authors allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information.

TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.

TL;DR: The main algorithms for color texture, shape and sketch query that are presented, show example query results, and discuss future directions are presented.

TL;DR: The Query by Image Content (QBIC) system as mentioned in this paper allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information.

TL;DR: A set of novel features and similarity measures allowing query by image content, together with the QBIC system, and a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance.