We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing the MRF. We make our code available on-line.

In the last decade, due to high resolution cameras and accurate meta - phase analyzes, the accuracy of chromosome classification has improved substantially. However, current Karyotyping systems demand large number of high quality train data to have an adequa tely plausible Precision per each chromosome. Such provision of high quality train data with accurate devices are not yet accomplished in some out - reached pathological laboratories. To prevent false positive detections in low - cost systems and low - quality i mages settings, this paper improves the classification Precision of chromosomes using proposed reliability thresholding metrics and deliberately engineered features. The proposed method has been evaluated using a variation of deep Alex - Net neural network, SVM, K - Nearest - Neighbors, and their cascade pipelines to an automated filtering of semi - straight chromosome. The classification results have highly improved over 90% for the chromosomes with more common defections and translocations. Furthermore, a compara tive analysis over the proposed thresholding metrics has been conducted and the best metric is bolded with its salient characteristics. The high Precision results provided for a very low - quality G - banding database verifies suitability of the proposed metri cs and pruning method for Karyotyping facilities in poor countries and low - budget pathological laboratories. Keywords: G - banded Karyotyping, Precision, Reliability metrics, Pattern Recognition, Medical Imaging 1 Introduction One of the ways to study and dia gnose birth - defects and biological disorders is through using Cytogenetics. This branch of science endeavors to analyze chromosome shapes and patterns to find out common defects. The methods used for such analyzes includes G - Banding, Fluorescent In - Situ Hy bridization (FISH), Comparative Genomic Hybridization (CGH) and Chromosome - specific unique - sequence probes [27] . While Molecular Cytogenetics methods are effective in biological disorders, they do not necessarily manifest specific chromosome defects. FISH methods, though having higher accuracy results in stains, are costly and unable to identify all chromosome abnorm alities. Being temporary in sustaining fluorescence detector, they demand higher provision effort and substance supply that might not be affordable for some countries . Furthermore, detecting some abnormalities implies having G - banding technique involved an d not merely using stains.

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We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing the MRF. We make our code available on-line.

We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing the MRF. We make our code available on-line. Papers published at the Neural Information Processing Systems Conference.