A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
The nature of statistical learning theory
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Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
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ACM Computing Surveys (CSUR)
IRM: integrated region matching for image retrieval
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Histogram refinement for content-based image retrieval
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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
Wavelet correlogram: A new approach for image indexing and retrieval
Pattern Recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Modified color motif co-occurrence matrix for image indexing and retrieval
Computers and Electrical Engineering
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This paper compares fixed partitioning and salient points schemes for dividing an image into patches, in combination with low-level MPEG-7 visual descriptors to represent the patches with particular patterns. A clustering technique is applied to construct a compact representation by grouping similar patterns into a cluster codebook. The codebook will then be used to encode the patterns into visual keywords. In order to obtain high-level information about the relational context of an image, a correlogram is constructed from the spatial relations between visual keyword indices in an image. For classifying images a k-nearest neighbors (k-NN) and a support vector machine (SVM) algorithm are used and compared. The techniques are compared to other methods on two well-known datasets, namely Corel and PASCAL. To measure the performance of the proposed algorithms, average precision, a confusion matrix, and ROC-curves are used. The results show that the cluster correlogram outperforms the cluster histogram. The saliency based scheme performs similarly to the fixed partitioning scheme and the SVM significantly outperforms the k-NN classifier. Finally, we demonstrate the robustness to noise, photometric, and geometric distortions.