Dynamic semantic feature-based long-term cross-session learning approach to content-based image retrieval

2012 ◽  
Author(s):  
Zhongmiao Xiao ◽  
Matthew J. Clark ◽  
KokSheik Wong ◽  
Xiaojun Qi
Keyword(s):  
Image Retrieval ◽  
Semantic Feature ◽  
Content Based Image Retrieval ◽  
Learning Approach ◽  
Dynamic Semantic ◽  
Feature Based

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

A Scalable Graph-Based Semi-Supervised Ranking System for Content-Based Image Retrieval

2013 ◽  
Vol 4 (4) ◽  
pp. 15-34
Author(s):  
Xiaojun Qi ◽  
Ran Chang
Keyword(s):  
Image Retrieval ◽  
Semantic Features ◽  
Dynamic Feedback ◽  
Ranking System ◽  
Retrieval Systems ◽  
Feature Based ◽  
Semantic Spaces ◽  
Supervised Ranking ◽  
High Level

The authors propose a scalable graph-based semi-supervised ranking system for image retrieval. This system exploits the synergism between relevance feedback based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Active learning is applied to build a dynamic feedback log to extract semantic features of images. Two-layer manifold graphs are then built in both low-level visual and high-level semantic spaces. One graph is constructed at the first layer using anchor images obtained from the feedback log. Several graphs are constructed at the second layer using images in their respective cluster formed around each anchor image. An asymmetric relevance vector is created for each second layer graph by propagating initial scores from the first layer. These vectors are fused to propagate relevance scores of labeled images to unlabeled images. The authors’ extensive experiments demonstrate the proposed system outperforms four manifold-based and five state-of-the-art long-term-based image retrieval systems.

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