Implicit visual concept modeling in image / video annotation

Authors:
Klimis Ntalianis;Anastasios Doulamis;Nicolas Tsapatsoulis
Affiliations:
Cyprus University of Technology, Limmasol, CT, Cyprus;Technical University of Crete, Chania 73100 , Greece;Cyprus University of Technology, Limmasol, Cyprus
Venue:
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Year:
2010

Citing 11
Cited 0

Semantic Annotation of Sports Videos

IEEE MultiMedia
CLARANS: A Method for Clustering Objects for Spatial Data Mining

IEEE Transactions on Knowledge and Data Engineering
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis

International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Accurately interpreting clickthrough data as implicit feedback

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Classifying Images from Athletics Based on Spatial Relations

SMAP '07 Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization
Usefulness of quality click-through data for training

Proceedings of the 2009 workshop on Web Search Click Data
Image annotation using clickthrough data

Proceedings of the ACM International Conference on Image and Video Retrieval
Using probabilistic latent semantic analysis for personalized web search

APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Integrated Mining of Visual Features, Speech Features, and Frequent Patterns for Semantic Video Annotation

IEEE Transactions on Multimedia
Non-sequential video content representation using temporal variation of feature vectors

IEEE Transactions on Consumer Electronics
Learning a semantic space from user's relevance feedback for image retrieval

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a novel approach for automatically annotating image databases is proposed. Despite most current approaches that are just based on spatial content analysis, the proposed method properly combines implicit feedback information and visual concept models for semantically annotating images. Our method can be easily adopted by any multimedia search engine, providing an intelligent way to even annotate completely non-annotated content. The proposed approach currently provides very interesting results in limited-content environments and it is expected to add significant value to billions of non-annotated images existing in the Web. Furthermore expert annotators can gain important knowledge relevant to user new trends, language idioms and styles of searching.