Progressive image coding for improved perceptual quality and recognition at low bit rates
Imaging and vision systems
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
An image based interactive digital library of mechanical engineering objects
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
An integrated aurora image retrieval system: AuroraEye
Journal of Visual Communication and Image Representation
Aerial photograph image retrieval using the MPEG-7 texture descriptors
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Adaptive image classification for aerial photo image retrieval
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Clustering-based feature selection for content based remote sensing image retrieval
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
| Hi-index | 0.01 |
We describe a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size. In addition, this paper also introduces a methodology to evaluate the performance of the algorithms in the prototype system. There are two major contributions: we suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement and image compression, as well as information analysis techniques such as the self-organizing map (SOM) into an effective large-scale geographical image retrieval system. We present two experiments that evaluate the performance of the Gabor-filter-extracted features along with the corresponding similarity measure against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model