A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition
This work addresses the problem of retrieving similar images based on shape for users in computer vision, but it is incremental as it builds on existing feature fusion approaches.
The paper tackled shape recognition by proposing a fusion method that combines local and global shape features into a composite descriptor, using a weighted ranking algorithm for similarity retrieval; experimental results showed it effectively discriminates between geometrically similar and non-similar shapes.
Retrieving similar images from a large dataset based on the image content has been a very active research area and is a very challenging task. Studies have shown that retrieving similar images based on their shape is a very effective method. For this purpose a large number of methods exist in literature. The combination of more than one feature has also been investigated for this purpose and has shown promising results. In this paper a fusion based shapes recognition method has been proposed. A set of local boundary based and region based features are derived from the labeled grid based representation of the shape and are combined with a few global shape features to produce a composite shape descriptor. This composite shape descriptor is then used in a weighted ranking algorithm to find similarities among shapes from a large dataset. The experimental analysis has shown that the proposed method is powerful enough to discriminate the geometrically similar shapes from the non-similar ones.