The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting the JPEG Compression Scheme for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Image retrieval by hypertext links
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Multiple evidence combination in image retrieval: Diogenes searches for people on the Web
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning-based linguistic indexing of pictures with 2--d MHMMs
Proceedings of the tenth ACM international conference on Multimedia
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Generic image classification using visual knowledge on the web
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A bootstrapping approach to annotating large image collection
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
SEVA: sensor-enhanced video annotation
Proceedings of the 13th annual ACM international conference on Multimedia
Graph based multi-modality learning
Proceedings of the 13th annual ACM international conference on Multimedia
Similarity space projection for web image search and annotation
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
PARAgrab: a comprehensive architecture for web image management and multimodal querying
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Web image annotation by fusing visual features and textual information
Proceedings of the 2007 ACM symposium on Applied computing
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Web Image Clustering Based on Multi-instance
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Automatic Web Image Annotation via Web-Scale Image Semantic Space Learning
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
SEVA: Sensor-enhanced video annotation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Deriving image-text document surrogates to optimize cognition
Proceedings of the 9th ACM symposium on Document engineering
Webpage segmentation for extracting images and their surrounding contextual information
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Ontology-Based Semantic Web Image Retrieval by Utilizing Textual and Visual Annotations
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Exploring Flickr's related tags for semantic annotation of web images
Proceedings of the ACM International Conference on Image and Video Retrieval
Web image mining using concept sensitive Markov stationary features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Region-based automatic web image selection
Proceedings of the international conference on Multimedia information retrieval
A method for processing the natural language query in ontology-based image retrieval system
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
Web image gathering with a part-based object recognition method
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Improved video categorization from text metadata and user comments
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Retrieving and ranking unannotated images through collaboratively mining online search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Boosting cross-media retrieval by learning with positive and negative examples
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Semi-supervised image classification for automatic construction of a health image library
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
A broadcast model for web image annotation
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Image indexing based on web page segmentation and clustering
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
Concept-based indexing of annotated images using semantic DNA
Engineering Applications of Artificial Intelligence
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
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Most current image retrieval systems and commercial search engines use mainly text annotations to index and retrieve WWW images. This research explores the use of machine learning approaches to automatically annotate WWW images based on a predefined list of concepts by fusing evidences from image contents and their associated HTML text. One major practical limitation of employing supervised machine learning approaches is that for effective learning, a large set of labeled training samples is needed. This is tedious and severely impedes the practical development of effective search techniques for WWW images, which are dynamic and fast-changing. As web-based images possess both intrinsic visual contents and text annotations, they provide a strong basis to bootstrap the learning process by adopting a co-training approach involving classifiers based on two orthogonal set of features -- visual and text. The idea of co-training is to start from a small set of labeled training samples, and successively annotate a larger set of unlabeled samples using the two orthogonal classifiers. We carry out experiments using a set of over 5,000 images acquired from the Web. We explore the use of different combinations of HTML text and visual representations. We find that our bootstrapping approach can achieve a performance comparable to that of the supervised learning approach with an F1 measure of over 54%. At the same time, it offers the added advantage of requiring only a small initial set of training samples.