Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Generic image classification using visual knowledge on the web
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Image and Feature Co-Clustering
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Grouping WWW Image Search Results by Novel Inhomogeneous Clustering Method
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Real time google and live image search re-ranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
A novel approach for filtering junk images from google search results
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Content-based image retrieval (CBIR) has been a challenging problem and its performance relies on the efficiency in modeling the underlying content and the similarity measure between the query and the retrieved images. Most existing metrics evaluate pairwise image similarity based only on image content, which is denoted as content similarity. However, other schemes utilize the annotations and the surrounding text to improve the retrieval results. In this study we refer to content as the visual and the textual information belonging to an image. We propose a representation of an image surrounding text in terms of concepts by utilizing an online knowledge source, e.g., Wikipedia, and propose a similarity metric that takes into account the new conceptual representation of the text. Moreover, we combine the content information with the contexts of an image to improve the retrieval percentage. The context of an image is built by constructing a vector with each dimension representing the content (visual and textual/conceptual) similarity between the image and any image in the collection. The context similarity between two images is obtained by computing the similarity between the corresponding context vectors using the vector similarity functions. Then, we fuse the similarity measures into a unified measure to evaluate the overall image similarity. Experimental results demonstrate that the new text representation and the use of the context similarity can significantly improve the retrieval performance.