Information Retrieval in large digital document repositories is at the same time a hard and crucial task. While the primary type of information available in documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide. Additionally image processing techniques are usually very demanding in computational resources. Hence, only recently the area of Content-Based Image Retrieval has gained more attention. In this paper we describe a new technique to identify known objects in a picture based on a comparison of the shapes to known models. The comparison works by progressive approximations to save computational resources, and relies on novel algorithmic and representational solutions to improve preliminary shape extraction.
[1]
T. A. Carlson,et al.
The State of the Art in Visual Object Recognition
,
2006
.
[2]
Johann Wolfgang.
Project Semacode: A Scale-invariant Object Recognition System for Content-based Queries in Images Databases
,
1999
.
[3]
Guojun Lu,et al.
A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval
,
2003,
J. Vis. Commun. Image Represent..
[4]
Richard Szeliski,et al.
Computer Vision - Algorithms and Applications
,
2011,
Texts in Computer Science.
[5]
Sudeep Sarkar,et al.
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
,
1997,
IEEE Trans. Pattern Anal. Mach. Intell..
[6]
Matthijs C. Dorst.
Distinctive Image Features from Scale-Invariant Keypoints
,
2011
.