International Journal of Computer Vision
Handbook of pattern recognition & computer vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Grid-Based Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A multi-feature optimization approach to object-based image classification
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Relevance feedback in region-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
An Object- and User-Driven System for Semantic-Based Image Annotation and Retrieval
IEEE Transactions on Circuits and Systems for Video Technology
A Biologically Inspired System for Classification of Natural Images
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, a new method for object-based image retrieval is proposed. The technique is designed to adaptively and efficiently locate salient blocks in images. Salient blocks are used to represent semantically meaningful objects in images and to perform object-oriented annotation and retrieval. An algorithm is proposed to locate the most suitable blocks of arbitrary size representing the query concept or object of interest in images. To annotate single objects according to human perception, associations between several low-level patterns and semantic concepts are modelled by an optimised multi-descriptor space. The approach starts by dividing the image into blocks partitioned according to several different layouts. Then, a fitting block is selected according to a similarity metric acting on concept-specific multi-feature spaces. The similarity metric is defined as linear combination of single feature space metrics for which the corresponding weights are learned from a group of representative salient blocks using multi-objective optimisation. Relevance Feedback is seamlessly integrated in the retrieval process. In each iteration, the user selects images relevant to the query object, then the corresponding salient blocks in selected images are used as training examples. The proposed technique was thoroughly assessed and selected results are reported in this paper to demonstrate its performance.