Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supporting Ranked Boolean Similarity Queries in MARS
IEEE Transactions on Knowledge and Data Engineering
A Weighted Distance Approach to Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
A comparison of score, rank and probability-based fusion methods for video shot retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
IEEE Transactions on Image Processing
Retrieval of images from artistic repositories using a decision fusion framework
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
IEEE Transactions on Circuits and Systems for Video Technology
Modified color motif co-occurrence matrix for image indexing and retrieval
Computers and Electrical Engineering
Content-based image retrieval by integrating color and texture features
Multimedia Tools and Applications
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Feature aggregation is a critical technique in content-based image retrieval (CBIR) systems that employs multiple visual features to characterize image content. Most previous feature aggregation schemes apply parallel topology, e.g., the linear combination scheme, which suffer from two problems. First, the function of individual visual feature is limited since the ranks of the retrieved images are determined only by the combined similarity. Second, the irrelevant images seriously affect the retrieval performance of feature aggregation scheme since all images in a collection will be ranked. To address these problems, we propose a new feature aggregation scheme, series feature aggregation (SFA). SFA selects relevant images using visual features one by one in series from the images highly ranked by the previous visual feature. The irrelevant images will be effectively filtered out by individual visual features in each stage, and the remaining images are collectively described by all visual features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that the proposed SFA can outperform conventional parallel feature aggregation schemes.