Content based Object Observation for Image Retrieval

Content based Object Observation for Image Retrieval

Vision is the most advance of our senses, so it is not surprising that image play the single most important role in human perception. A logical place of overlap of image's processing and their analysis is recognition of object. To extract attributes from image up to include the reorganization of individual object, analysis of text, extracting characters, describing, recognition that and th...

Object-driven content-based image retrieval

This paper presents a novel unsupervised strategy for content-based image retrieval. It is based on a meaningful segmentation procedure that can provide proper distributions for matching via the Earth mover's distance as a similarity metric. The segmentation procedure is based on a hierarchical watershed-driven algorithm that extracts meaningful regions automatically. In this framework, the pro...

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