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Relative spatial features for image memorability
The Weighted Object Area (WOA) that jointly considers the location and size of objects and the Relative Area Rank (RAR) that captures the relative unusualness of the size of Objects are proposed and empirically demonstrate their useful correlation with the image memorability.
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
It is shown that the proposed method accelerates the speed of convergence of the ADMM by automatically deciding the constraint penalty needed for parameter consensus in each iteration, and also proposes an extension of the method that adaptively determines the maximum number of iterations to update the penalty.
Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms
Predicting Crowd Egress and Environment Relationships to Support Building Design Optimization
k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification
Results on ten public domain microarray data indicated that TSP family classifiers serve as good feature selection schemes which may be combined effectively with other classification methods.
Sentiment Flow for Video Interestingness Prediction
This work extends a recent pilot study on the video interestingness prediction by using a mid-level representation of sentiment (emotion) sequence and evaluates the proposed framework on three datasets including the datasets proposed by the pilot study and shows that the result effectively verifies a promising utility of the approach.
Mass Lesions Classification in Digital Mammography using Optimal Subset of BI-RADS and Gray Level Features
- Saejoon Kim, Sejong Yoon
- Computer Science6th International Special Topic Conference on…
- 26 December 2007
Computer-aided diagnosis of mass lesions in Digital Database for Screening Mammography is investigated using a recently developed SVM based on recursive feature elimination (SVM-RFE) as the classification technique, and results indicate that using only a subset of the available set of features facilitates increased computer- aided diagnosis accuracy.
The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation
It is shown how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints, and it is shown that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.
Distributed Probabilistic Learning for Camera Networks with Missing Data
This work shows how traditional centralized models, such as probabilistic PCA and missing-data PPCA, can be learned when the data is distributed across a network of sensors, and demonstrates the utility of this approach on the problem of distributed affine structure from motion.
A Hybrid Approach for Video Memorability Prediction
In this working note, we present our approach and investigation on the Predicting Media Memorability Task at MediaEval 2019. We used original video frames and caption data from the provided dataset,…