Fundamentals of digital image processing
Fundamentals of digital image processing
The C programming language
Digital image processing
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 C++
Programming Microsoft Visual C++
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
A Perspective View on Visual Information Retrieval Systems
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
UNIX Network Programming, Vol. 1
UNIX Network Programming, Vol. 1
Advanced Programming in the UNIX(R) Environment (2nd Edition)
Advanced Programming in the UNIX(R) Environment (2nd Edition)
Categorization of natural scenes: local vs. global information
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Content Based Image Retrieval Using Color, Texture and Shape Features
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
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In Content Based Image Retrieval (CBIR) domain a new methodology is proposed in this paper; this methodology is based on statistical features such as Hu invariant moments (invariant to scale and translation) and a correlation measure (between 2 images). In order to attack image rotation problem, we use principal components analysis. Image comparison is done by correlation. Proposed scheme was tested with hundred of structured images like: electronic circuits, cell phones, cars, trees, leaves, grass, glasses, tables, etc. Features characteristics are extracted from invariant moments, taken from window size estimation. From a query image, a set of features were estimated in order to compare to a set of images. Correlation function is applied to get image similarity, it is obtained a believe percentage value. As a methodology conclusion, we found that the invariant moments combined with principal component analysis gives excellent results in image retrieval task. An exhaustive study was performed with 1000 images. We also evaluated the impact of noise on the images testing additive Gaussian random noise.