Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201

Urban objects classification using Mueller matrix polarimetry and machine learning

Detecting and recognizing different kinds of urban objects is an important problem, in particular, in autonomous driving. In this context, we studied the potential of Mueller matrix polarimetry for classifying a set of relevant real-world objects: vehicles, pedestrians, traffic signs, pavements, vegetation and tree trunks. We created a database with their experimental Mueller matrices measured at 1550 nm and trained two machine learning classifiers, support vector machine and artificial neural network, to classify new samples. The overall accuracy of over 95% achieved with this approach, with either models, reveals the potential of polarimetry, specially combined with other remote sensing techniques, to enhance object recognition.European Regional Development Fund (POCI-01-0247-FEDER-037902); Fundação para a Ciência e a Tecnologia (UIDB/04650/2020).This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Program (COMPETE 2020) [Project n° 037902; Funding Reference: POCI-01-0247-FEDER-037902] and partially supported by the Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UIDB/04650/2020. The authors acknowledge Alexandre Correia and Moisés Duarte (Bosch Car Multimedia Portugal S.A) and Dr. Rui Pereira and Dr. Stéphane Clain (Minho University) for fruitful discussions on data analysis. The authors also acknowledge city council of Braga (Portugal) for the supply of samples

Objects classification in still images using the region covariance descriptor

The goal of the Object Classification is to classify the objects in images. Classification aims for the recognition of generic classes, which is also known as Generic Object Recognition. This is quite different from Specific Object Recognition, such as recognizing specific person, own car, and etc. Human beings are generally better in recognizing generic classes than specific objects. Classification is a much harder problem to solve by artificial systems. Classification algorithm must be robust to changes in illumination, object scale, view point, and etc. The algorithm also has to manage large intra class variations and small inter class variations. In recent literature, some of the classification methods use Bag of Visual Words model. In this work the main emphasis is on region descriptor and representation of training images. Given a set of training images, interest points are detected through interest point detectors. Region around an interest point is described by a descriptor. Region covariance descriptor is adopted from porikli et al. [21], where they used this descriptor for object detection and classification. This region covariance descriptor is combined with Bag of Visual words model. We have used a different set of features for Classification task. Covariance of d-features, e.g. spatial location, Gaussian kernel with three different s values, first order Gaussian derivatives with two different s values, and second order Gaussian derivatives with four different s values, characterizes a region of interest. An image is also represented by Bag of Visual words obtained with both SIFT and Covariance descriptors. We worked on five datasets; Caltech-4, Caltech-3, Animal, Caltech-10, and Flower (17 classes), with first four taken from Caltech-256 and Caltech-101 datasets. Many researchers used Caltech-4 dataset for object classification task. The region covariance descriptor is outperforming SIFT descriptor on both Caltech-4 and Caltech-3 datasets, whereas Combined representation (SIFT + Covariance) is outperforming both SIFT and Covarianc

Detection of Mines in Acoustic Images using Higher Order Spectral Features

A new pattern-recognition algorithm detects approximately 90% of the mines hidden in the Coastal Systems Station Sonar0, 1, and 3 databases of cluttered acoustic images, with about 10% false alarms. Similar to other approaches, the algorithm presented here includes processing the images with an adaptive Wiener filter (the degree of smoothing depends on the signal strength in a local neighborhood) to remove noise without destroying the structural information in the mine shapes, followed by a two-dimensional FIR filter designed to suppress noise and clutter, while enhancing the target signature. A double peak pattern is produced as the FIR filter passes over mine highlight and shadow regions. Although the location, size, and orientation of this pattern within a region of the image can vary, features derived from higher order spectra (HOS) are invariant to translation, rotation, and scaling, while capturing the spatial correlations of mine-like objects. Classification accuracy is improved by combining features based on geometrical properties of the filter output with features based on HOS. The highest accuracy is obtained by fusing classification based on bispectral features with classification based on trispectral features