International Journal of Computer Vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Cartoon Image Classification Based on Wavelet Transform
APCIP '09 Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In recent years, thousands of images are generated everyday, which implies the necessity to classify, organize and access them by easy and faster way. The need for image classification is becoming increasingly important. Classifying the images into semantic categories is a challenging problem. Although this is usually not a very difficult task for humans, it has been proved to be an extremely difficult problem for computer programs. This paper presents the idea of using Discrete Cosine Transform to generate the feature vector for Image Classification. The various sizes of feature vectors are generated such as 8X8, 16X16, 32X32, 64X64 and 128X128. The proposed algorithm is worked over database of 1000 images spread over 10 different classes. The Euclidean distance is used as similarity measure. A threshold value is set to determine to which category the query image belongs to.