A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Technical Symbols Recognition Using the Two-Dimensional Radon Transform
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Proceedings of the 2004 ACM symposium on Applied computing
Canny Edge Detection Enhancement by Scale Multiplication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Shape Matching Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating image data into biomedical text categorization
Bioinformatics
Bioinformatics
An Effective Edge Detection Methodology for Medical Images Based on Texture Discrimination
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Figure mining for biomedical research
Bioinformatics
An efficient shape based feature for retrieval of healthcare literatures using CBIR technique
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Content based image retrieval using various distance metrics
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
Figure Retrieval in Biomedical Literature
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
| Hi-index | 0.00 |
Multi-modal and Unstructured nature of documents make their retrieval from healthcare document repositories a challenging task. Text based retrieval is the conventional approach used for solving this problem. In this paper, the authors explore an alternate avenue of using embedded figures for the retrieval task. Usually, context of a document is directly reflected in the associated figures, therefore embedded text within these figures along with image features have been used for similarity based retrieval of figures. The present work demonstrates that image features describing the structural properties of figures are sufficient for the figure retrieval task. First, the authors analyze the problem of figure retrieval from biomedical literature and identify significant classes of figures. Second, they use edge information as a means to discriminate between structural properties of each figure category. Finally, the authors present a methodology using a novel feature descriptor namely Fourier Edge Orientation Autocorrelogram FEOAC to describe structural properties of figures and build an effective Biomedical document retrieval system. The experimental results demonstrate the better retrieval performance and overall improvement of FEOAC for figure retrieval task, especially when most of the edge information is retained. Apart from invariance to scale, rotation and non-uniform illumination, the proposed feature descriptor is shown to be relatively robust to noisy edges.