Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Features in Content-based Image Retrieval Systems: a Survey
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
The Truth about Corel - Evaluation in Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Robust Object Recognition in Images and the Related Database Problems
Multimedia Tools and Applications
On combining graph-partitioning with non-parametric clustering for image segmentation
Computer Vision and Image Understanding
A comparison of active classification methods for content-based image retrieval
Proceedings of the 1st international workshop on Computer vision meets databases
Computer Vision and Image Understanding
Approximation of linear discriminant analysis for word dependent visual features selection
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
LDA/SVM driven nearest neighbor classification
IEEE Transactions on Neural Networks
Proceedings of the 6th ACM international conference on Image and video retrieval
| Hi-index | 0.00 |
To automatically determine semantics of a shape or to generate a set of keywords that describe the content of a given image is a difficult problem due to: (a) the high-dimensional problem, (b) the unsolved automatic object segmentation (mis-segmentation), and (c) the lack of well-labeled large image database (mis-labeling). In order to tackle (a), despite (b), (c) and the expensive handy image segmentation and labeling, visual features should be automatically selected to convey the most robust and discriminant information without requiring too computational cost. Therefore, we propose a novel method: 'Approximation of Linear Discriminant Analysis' (ALDA), which is more generic than LDA: ALDA does not require explicit class labeling of each training samples. We theoretically show that under weak assumption, ALDA allows efficient ranking estimation of the discriminant powers of the visual features. We apply ALDA on COREL database (10K images, 267 words) with Normalized Cuts segmentation algorithm. First, we demonstrate an image classification gain of 43%, while reducing features set by a factor 10. Secondly, we demonstrate that for some words (like 'Door', 'Flag'), even low-level shape features (convex hull, or moment of inertia) are more discriminant than any color or texture features.