Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Image Retrieval Using HMMs
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Face Database Retrieval Using Pseudo 2D Hidden Markov Models
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Fast Searching of Digital Face Libraries Using Binary Image Metrics
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Imlooking: image-based face retrieval in online dating profile search
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image segmentation using information bottleneck method
IEEE Transactions on Image Processing
Complex wavelet structural similarity: a new image similarity index
IEEE Transactions on Image Processing
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In this paper, we propose a novel metrics for statistical features of images based on Information Bottleneck principle (IBP). Rather than measure the differences among images with classical distance, our model takes the attributes of feature space into consideration. Through evaluating the loss of information of image database, our model is especially designed for the type of features bearing statistical attributes such as histograms, moments etc. The statistical feature is adopted to denote the information of the image database and our metrics measures the distance between two images with the amount of decreased information due to combine them as one category. The proposed metrics is validated in face retrieval with the dominant Local Binary Pattern (LBP) feature. Experimental results on FERET face database show that our model possesses preferable performance.