Comparison Of Feature Vectors Research Articles

Handling of “unknown unknowns” - classification of 3D geometries from CAD open set datasets using Convolutional Neural Networks

This paper refers to the application of Convolutional Neural Networks (CNNs) for the classification of 3D geometries from Computer-Aided Design (CAD) datasets with a large proportion of unknown unknowns (classes unknown after training). The motivation of the work is the automatic recognition of standard parts in the large CAD-based image data set and thus, reducing the time required for the manual preparation of the data set. The classification is based on a threshold value of the Softmax output layer (first criterion), as well as on three different methods of a second criterion. The three methods for the second criterion are the comparison of metadata relating to the geometries, the comparison of feature vectors from previous dense layers of the CNN with a Spearman correlation, and the distance-based difference between multivariate Gaussian models of these feature vectors using Kullback-Leibler divergence. It is confirmed that all three methods are suitable to solve an open set problem in large 3D datasets (more than 1000 different geometries). Classification and training are image-based using different multi-view representations of the geometries.

Image retrieval system based on multi feature extraction and its performance assessment

The main problem in the Content based image retrieval is “semantic gap”. A human match two images related to its semantics, while a retrieval system do the same process based on comparison of feature vectors corresponding to visual image features. The problem in retieval of images is identifying its unique features. This article proposes three different methods to retrieve images based on the unique features of an image and also evaluates the retrieval system performance. The first approach called Surrounding information retrieval (SIR) extracts the features related to the similarities of the neighborhood intensity values. The second approach Minimum edge retrieval (MER) identifies the minimum intensity value of each block. The third approach integrated feature retrieval (IFR) combines the properties of feature extraction from SIR and MER. The extracted unique features are stored in a feature dataset and the similar images are retrieved by comparing the dataset using distance measure. The performance of retrieval system is calculated in terms of its recall and precision. The precision and recall values are superior than the existing methods. The method IFR with multi feature extraction shows good in retrieval accuracy compared with other methods.

A comprehensive analysis of the parameters in the creation and comparison of feature vectors in distributional semantic models for multiple languages

Measuring the semantic similarity and relatedness of words is important for many natural language processing tasks. Although distributional semantic models designed for this task have many different parameters, such as vector similarity measures, weighting schemes and dimensionality reduction techniques, there is no truly comprehensive study simultaneously evaluating these parameters while also analysing the differences in the findings for multiple languages. We would like to address this gap with our systematic study by searching for the best configuration in the creation and comparison of feature vectors in distributional semantic models for English, Spanish and Hungarian separately, and then comparing our findings across these languages. During our extensive analysis we test a large number of possible settings for all parameters, with more than a thousand novel variants in case of some of them. As a result of this we were able to find such configurations that significantly outperform conventional configurations and achieve state-of-the-art results.

A Comprehensive Study of the Parameters in the Creation and Comparison of Feature Vectors in Distributional Semantic Models

ABSTRACT Measuring the semantic similarity and relatedness of words can play a vital role in many natural language processing tasks. Distributional semantic models computing these measures can have many different parameters, such as different weighting schemes, vector similarity measures, feature transformation functions and dimensionality reduction techniques. Despite their importance there is no truly comprehensive study simultaneously evaluating the numerous parameters of such models, while also considering the interaction of these parameters with each other. We would like to address this gap with our systematic study. Taking the necessary distributional information extracted from the chosen dataset as already granted, we evaluate all important aspects of the creation and comparison of feature vectors in distributional semantic models. Testing altogether 10 parameters simultaneously, we try to find the best combination of parameter settings, with a large number of settings examined in case of some of the parameters. Beside evaluating the conventionally used settings for the parameters, we also propose numerous novel variants, as well as novel combinations of parameter settings, some of which significantly outperform the combinations of settings in general use, thus achieving state-of-the-art results.

Performance Comparison of Wavelet Transform with respect to Their Transforms of CBVR

Content-based search and retrieval of video data has become a challenging and important issue. Different methods[1,2,3] have been proposed to develop video retrievals systems to achieve better performance in terms of accuracy. The proposed technique uses wavelettransforms that areCosine Wavelet Transform, Walsh, Kekre, Slant and Hartley Wavelet Transform. The benefit of energy compaction of fewer transformedcoefficients is taken to reduce the feature vector size by taking fractional coefficients[5]of transformed frames of video. Smaller feature vector size results in less time for comparison of feature vectors resulting in faster retrieval of images. The feature vectors are extracted and coefficients sets are considered as feature vectors (100%, 6.25%, 3.125%, 1.5625%, 0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012%, 0.006% and 0.003% of complete transformed coefficients).The database consists of 500 videos spread across 10 categories.Performance using wavelets is compared with respect to their transforms. In all considered wavelet transform, experimental result show the performance improvement in fractional coefficient of wavelet transform in content based video retrieval as compared to 100% coefficient considered.

Comparison of Feature Vectors in Keystroke Dynamics

A number of current applications require algorithms able to extract a model from one-class data and classify unseen data as self or non-self in a novelty detection scenario, such as spam identification and intrusion detection. In this paper the authors focus on keystroke dynamics, which analyses the user typing rhythm to improve the reliability of user authentication process. However, several different features may be extracted from the typing data, making it difficult to define the feature vector. This problem is even more critical in a novelty detection scenario, when data from the negative class is not available. Based on a keystroke dynamics review, this work evaluated the most used features and evaluated which ones are more significant to differentiate a user from another using keystroke dynamics. In order to perform this evaluation, the authors tested the impact on two benchmark databases applying bio-inspired algorithms based on neural networks and artificial immune systems.

Classification Performance Comparison ofFeature Vectors Based on Summation Schemeand Maximization Scheme

Classification Performance Comparison ofFeature Vectors Based on Summation Schemeand Maximization Scheme