Instance-Based Learning Algorithms
Machine Learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth 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
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Comparison of Texture Features Based on Gabor Filters
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Improving document representations using relevance feedback: the RFA algorithm
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Image retrieval based on indexing and relevance feedback
Pattern Recognition Letters
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
Detection of documentary scene changes by audio-visual fusion
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
IEEE Transactions on Multimedia
A unified framework for image retrieval using keyword and visual features
IEEE Transactions on Image Processing
Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting
Engineering Applications of Artificial Intelligence
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Accurate and fast retrieval of relevant images is a challenging task mainly due to the limitation in understanding hidden knowledge in images, known as semantic gap. In this work, we propose a novel approach which incorporates local feature representation for retrieval of grey and colour images from an archive with user intervention. We used histogram features, which are computationally efficient, hence resulting in quick image retrieval. The computed image feature vectors are used for similarity matching with weighted feed-backed image retrieval. We experimented both on publicly available and annotated image data sets to illustrate the effectiveness of our approach.