A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test
IEEE Transactions on Knowledge and Data Engineering
Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback
Multimedia Tools and Applications
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Automatic function selection for large scale salient object detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Detection of visual attention regions in images using robust subspace analysis
Journal of Visual Communication and Image Representation
A ROI image retrieval method based on CVAAO
Image and Vision Computing
Fast color-spatial feature based image retrieval methods
Expert Systems with Applications: An International Journal
Image retrieval using sub-image matching in photos using MPEG-7 descriptors
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Fast query point movement techniques with relevance feedback for content-based image retrieval
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Visual query attributes suggestion
Proceedings of the 20th ACM international conference on Multimedia
A new ROI based image retrieval system using an auxiliary Gaussian weighting scheme
Multimedia Tools and Applications
Information Sciences: an International Journal
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Query-by-example is the most popular query model in recent content-based image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.