Efficient image retrieval by fuzzy rules from boosting and metaheuristic

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter. © 2020 Marcin Korytkowski et al., published by Sciendo.program of the Polish Minister of Science and Higher Education under the name "Regional Initiative of Excellence" in the years 2019-2022 [020/RID/2018/19

A Survey of Factors Influencing MLP Error Surface

Visualization of neural network error surfaces and learning trajectories helps to understand the influence of numerous factors on the neural learning process. This understanding can be used to improve training and design of MLP networks. The following topics are discussed using a few benchmark datasets for illustration: general error surface properties including local minima, plateaus and narrow funnels, their dependence on network structure, input data, transfer and error functions, consequences of weight initialization, and interesting directions in the weight space. The error surfaces are shown in 3-dimensional PCA-based projections. Finally a possibility of e#ective weight number reduction is discussed

Extraction of prototype-based threshold rules using neural training procedure

Complex neural and machine learning algorithms usually lack comprehensibility. Combination of sequential covering with prototypes based on threshold neurons leads to a prototype-threshold based rule system. This kind of knowledge representation can be quite efficient, providing solutions to many classification problems with a single rule

Fast image index for database management engines

Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets

Efficient image retrieval by fuzzy rules from boosting and metaheuristic

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter

Rational Design of Small Molecule Inhibitors Targeting the Rac GTPase-p67phox Signaling Axis in Inflammation

SummaryThe NADPH oxidase enzyme complex, NOX2, is responsible for reactive oxygen species production in neutrophils and has been recognized as a key mediator of inflammation. Here, we have performed rational design and in silico screen to identify a small molecule inhibitor, Phox-I1, targeting the interactive site of p67phox with Rac GTPase, which is a necessary step of the signaling leading to NOX2 activation. Phox-I1 binds to p67phox with a submicromolar affinity and abrogates Rac1 binding and is effective in inhibiting NOX2-mediated superoxide production dose-dependently in human and murine neutrophils without detectable toxicity. Medicinal chemistry characterizations have yielded promising analogs and initial information of the structure-activity relationship of Phox-I1. Our studies suggest the potential utility of Phox-I class inhibitors in NOX2 oxidase inhibition and present an application of rational targeting of a small GTPase-effector interface