Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Machine vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition and image analysis
Pattern recognition and image analysis
Edge-based structural features for content-based image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Digital Image Processing
Texture classification using wavelet transform
Pattern Recognition Letters
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
An efficient color representation for image retrieval
IEEE Transactions on Image Processing
Qualitative evaluation of automatic assignment of keywords to images
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
CLAIRE: A modular support vector image indexing and classification system
ACM Transactions on Information Systems (TOIS)
Evaluation for uncertain image classification and segmentation
Pattern Recognition
Computers and Electronics in Agriculture
An adaptive level-selecting wavelet transform for texture defect detection
Image and Vision Computing
Reasoning Web
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
A fusion neural network classifier for image classification
Pattern Recognition Letters
Qualitative evaluation of automatic assignment of keywords to images
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Spectral edit distance method for image clustering
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Using color chains similarities for MLB sports image retrieval
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
A review on automatic image annotation techniques
Pattern Recognition
Histograms, wavelets and neural networks applied to image retrieval
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Natural / man-made object classification based on gabor characteristics
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Hierarchical classification of object images using neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Statistical object recognition including color modeling
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
On object classification: artificial vs. natural
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Detection and classification of areca nuts with machine vision
Computers & Mathematics with Applications
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
A new matching strategy for content based image retrieval system
Applied Soft Computing
The Visual Computer: International Journal of Computer Graphics
The Journal of Supercomputing
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In this paper, we propose a method of content-based image classification using a neural network. The images for classification are object images that can be divided into foreground and background. To deal with the object images efficiently, in the preprocessing step we extract the object region using a region segmentation technique. Features for the classification are shape-based texture features extracted from wavelet-transformed images. The neural network classifier is constructed for the features using the back-propagation learning algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 300 training data and 300 test data composed of 10 images from each of 30 classes shows classification rates of 81.7% and 76.7% correct, respectively.