Visual Saliency Detection Based on Multiscale Deep CNN Features

postprin

A unified learning framework for content based medical image retrieval using a statistical model

AbstractThis paper presents a unified learning framework for heterogeneous medical image retrieval based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA). Using the unified framework, the color autocorrelogram, edge orientation autocorrelogram (EOAC) and micro-texture information of medical images are extracted. The EOAC is constructed in HSV color space, to circumvent the loss of edges due to spectral and chromatic variations. The proposed system employed adaptive binary tree based support vector machine (ABTSVM) for efficient and fast classification of medical images in feature vector space. The Manhattan distance measure of order one is used in the proposed system to perform a similarity measure in the classified and indexed feature vector space. The precision and recall (PR) method is used as a measure of performance in the proposed system. Short-term based relevance feedback (RF) mechanism is also adopted to reduce the semantic gap. The Experimental results reveal that the retrieval performance of the proposed system for heterogeneous medical image database is better than the existing systems at low computational and storage cost

An accurate and interpretable model for siRNA efficacy prediction

BACKGROUND: The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging. RESULTS: We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions. CONCLUSION: The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web a

Leveraging the multimodal information from video content for video recommendation

Since the popularisation of media streaming, a number of video streaming services are continually buying new video content to mine the potential profit. As such, newly added content has to be handled appropriately to be recommended to suitable users. In this dissertation, the new item cold-start problem is addressed by exploring the potential of various deep learning features to provide video recommendations. The deep learning features investigated include features that capture the visual-appearance, as well as audio and motion information from video content. Different fusion methods are also explored to evaluate how well these feature modalities can be combined to fully exploit the complementary information captured by them. Experiments on a real-world video dataset for movie recommendations show that deep learning features outperform hand crafted features. In particular, it is found that recommendations generated with deep learning audio features and action-centric deep learning features are superior to Mel-frequency cepstral coefficients (MFCC) and state-of-the-art improved dense trajectory (iDT) features. It was also found that the combination of various deep learning features with textual metadata and hand-crafted features provide significant improvement in recommendations, as compared to combining only deep learning and hand-crafted features.Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021.The MultiChoice Research Chair of Machine Learning at the University of PretoriaUP Postgraduate Masters Research bursaryElectrical, Electronic and Computer EngineeringMEng (Computer Engineering)Unrestricte

Using Surfaces and Surface Relations in an Early Cognitive Vision System

The final publication is available at Springer via http://dx.doi.org/10.1007/s00138-015-0705-yWe present a deep hierarchical visual system with two parallel hierarchies for edge and surface information. In the two hierarchies, complementary visual information is represented on different levels of granularity together with the associated uncertainties and confidences. At all levels, geometric and appearance information is coded explicitly in 2D and 3D allowing to access this information separately and to link between the different levels. We demonstrate the advantages of such hierarchies in three applications covering grasping, viewpoint independent object representation, and pose estimation.European Community’s Seventh Framework Programme FP7/IC