Fundamentals of digital image processing
Fundamentals of digital image processing
The C programming language
UNIX network programming
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Programming Microsoft Visual Basic 6.0
Programming Microsoft Visual Basic 6.0
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Digital Image Restoration
Advanced Programming in the UNIX(R) Environment (2nd Edition)
Advanced Programming in the UNIX(R) Environment (2nd Edition)
| Hi-index | 0.00 |
In this work, an alternative image searching method is proposed. The method is based on principal components analysis and invariant moments (invariant to scale, rotation and translation). It was tested with hundreds of normal images: cell phones, faces, trees, girls, cats, dogs, etc. Principal components analysis is used to characterize an image, and the invariant moments technique is used to extract image features by window size estimation. From an incoming image, a set a features are estimated in order to compare it with a database of images. By using the Euclidean distance, and a Boolean exclusive or (XOR) operation, we obtain a percentage value of likeness. We found that the combination of principal components analysis and invariant moments give excellent results in image identification tasks. An exhaustive study was performed with 500 images. Results include tests over different image sizes and orientation. The robustness of the algorithm is also examined in terms of Gaussian noise.