LEARNING SCENE HIERARCHY: FROM CATEGORY-LEVEL SIMILARITY TO ATTRIBUTE-LEVEL SIMILARITY

Ph.DDOCTOR OF PHILOSOPHY (NUSGS

Estimation of a class of stochastic switching neural networks with sensor saturations through a nonsynchronous filter

In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring sensor saturations is investigated. A practical phenomenon of nonsynchronous jumps between RNNs modes and desired mode-dependent filters is considered and a nonstationary mode transition among the filters is used to model the nonsynchronous jumps to different degrees that are also mode-dependent. The sensor saturation occurs in a probabilistic way according to a Bernoulli sequence. Sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy to-peak performance index. A numerical example is presented to verify the theoretical findings

Scene hierarchy : a missing piece in scene classification

With the ultimate aim of semantic understanding of visual scenes, categorizing them into meaningful groups has been a central task in computer vision for more than two decades. From classical handcrafted features to recent self-learnt features, substantial progress has been made to improve its performance. Failed to be noticed by most solutions proposed so far, a hierarchical structure existing in scene categories, termed as scene hierarchy, is discovered in this paper. We argue that such hierarchy could reveal the latent relations among different visual scenes and further advance the state of the art of scene classification. In particular, we present a data-driven approach to identify the scene hierarchy and demonstrate its effectiveness in assisting scene classification

Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients

10.1186/s12871-021-01466-8BMC Anesthesiology21124