International Journal of Computer Vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Object Recognition Using Multidimensional Receptive Field Histograms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Invariant Features for Gray Scale Images
Mustererkennung 1995, 17. DAGM-Symposium
Object-based queries using color points of interest
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
| Hi-index | 0.00 |
The use of local features in computer vision has shown to be promising. Local features have several advantages including invariance to image transformations, independence of the background, and robustness in difficult situations like partial occlusions. In this paper we suggest using local integral invariants to extract local image descriptors around interest points and use them for the retrieval task. Integral invariants capture the local structure of the neighborhood around the points where they are computed. This makes them very well suited for constructing highly-discriminative local descriptors. We study two types of kernels used for extracting the feature vectors and compare the performance of both. The dimensionality of the feature vector to be used is investigated. We also compare our results with the SIFT features. Excellent results are obtained using a dataset that contains instances of objects that are viewed in difficult situations that include clutter and occlusion.