Balance-guaranteed optimized tree with reject option for live fish recognition

This thesis investigates the computer vision application of live fish recognition, which is needed in application scenarios where manual annotation is too expensive, when there are too many underwater videos. This system can assist ecological surveillance research, e.g. computing fish population statistics in the open sea. Some pre-processing procedures are employed to improve the recognition accuracy, and then 69 types of features are extracted. These features are a combination of colour, shape and texture properties in different parts of the fish such as tail/head/top/bottom, as well as the whole fish. Then, we present a novel Balance-Guaranteed Optimized Tree with Reject option (BGOTR) for live fish recognition. It improves the normal hierarchical method by arranging more accurate classifications at a higher level and keeping the hierarchical tree balanced. BGOTR is automatically constructed based on inter-class similarities. We apply a Gaussian Mixture Model (GMM) and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. This novel classification-rejection method cleans up decisions and rejects unknown classes. After constructing the tree architecture, a novel trajectory voting method is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. The proposed BGOTR-based hierarchical classification method is applied to recognize the 15 major species of 24150 manually labelled fish images and to detect new species in an unrestricted natural environment recorded by underwater cameras in south Taiwan sea. It achieves significant improvements compared to the state-of-the-art techniques. Furthermore, the sequence of feature selection and constructing a multi-class SVM is investigated. We propose that an Individual Feature Selection (IFS) procedure can be directly exploited to the binary One-versus-One SVMs before assembling the full multiclass SVM. The IFS method selects different subsets of features for each Oneversus- One SVM inside the multiclass classifier so that each vote is optimized to discriminate the two specific classes. The proposed IFS method is tested on four different datasets comparing the performance and time cost. Experimental results demonstrate significant improvements compared to the normal Multiclass Feature Selection (MFS) method on all datasets

Underwater Live Fish Recognition Using a Balance-Guaranteed Optimized Tree

Abstract. Live fish recognition in the open sea is a challenging multiclass classification task. We propose a novel method to recognize fish in an unrestricted natural environment recorded by underwater cameras. This method extracts 66 types of features, which are a combination of color, shape and texture properties from different parts of the fish and reduce the feature dimensions with forward sequential feature selection (FSFS) procedure. The selected features of the FSFS are used by an SVM. We present a Balance-Guaranteed Optimized Tree (BGOT) to control the error accumulation in hierarchical classification and, therefore, achieve better performance. A BGOT of 10 fish species is automatically constructed using the inter-class similarities and a heuristic method. The proposed BGOT-based hierarchical classification method achieves about 4 % better accuracy compared to state-of-the-art techniques on a live fish image dataset.

Quantum entanglement and disentanglement of multi-atom systems

We present a review of recent research on quantum entanglement, with special emphasis on entanglement between single atoms, processing of an encoded entanglement and its temporary evolution. Analysis based on the density matrix formalism are described. We give a simple description of the entangling procedure and explore the role of the environment in creation of entanglement and in disentanglement of atomic systems. A particular process we will focus on is spontaneous emission, usually recognized as an irreversible loss of information and entanglement encoded in the internal states of the system. We illustrate some certain circumstances where this irreversible process can in fact induce entanglement between separated systems. We also show how spontaneous emission reveals a competition between the Bell states of a two qubit system that leads to the recently discovered "sudden" features in the temporal evolution of entanglement. An another problem illustrated in details is a deterministic preparation of atoms and atomic ensembles in long-lived stationary squeezed states and entangled cluster states. We then determine how to trigger the evolution of the stable entanglement and also address the issue of a steered evolution of entanglement between desired pairs of qubits that can be achieved simply by varying the parameters of a given system.Comment: Review articl