Real-time Multi-object Face Recognition Using Content Based Image Retrieval (CBIR)

Content-based Image Retrieval by Ontology-based Object Recognition

The main disadvantage of image retrieval systems is their lack of domain knowledge. Therefore a retrieval system has to focus on primitive features, as Eakins and Graham name them [3]. Due to the lack of background knowledge of the domain, the retrieval error rate is usually dissatisfying or the search options are limited to syntactic queries. Knowledgebased techniques allow for semantical sear...

Image Background Search: Combining Object Detection Techniques with Content-Based Image Retrieval(CBIR) Systems

A framework for combining object detection techniques with a content-based image retrieval(CBIR) system is discussed. As an example, a special CBIR system which focuses on human faces as foreground and decides the similarity of images based on background features is presented. This system may be useful in automatically generating albums from consumer photos.

Structured Object Recognition for Content Based Image Retrieval

This report represents a novel method for content base image retrieval it uses saliency detection to identify foreground object and then extract features from those object so that system could train on those features to identify different classes. Linear Discernment Analysis is used to analyze data so that features could be projected to lower dimension to get a reasonable image representation. ...

Object and Concept Recognition for Content-Based Image Retrieval

Object and Concept Recognition for Content-Based Image Retrieval Yi Li Chair of the Supervisory Committee: Professor Linda G. Shapiro Computer Science & Engineering The problem of recognizing classes of objects in images is important for annotation and indexing of image and video databases. Users of commercial CBIR systems prefer to pose their queries in terms of key words. To help automate the...

Project Plan Structured Object Recognition for Content Based Image Retrieval