Walsh Transform based Feature vector generation for  Image Database Classification

Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques.

An Efficient Method for Similarity Measure in Independent PCA based Classification

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v9i3.3335 ◽

2013 ◽

Vol 9 (3) ◽

pp. 1099-1109

Author(s):

Dr. H. B. Kekre ◽

Dr. Tanuja K. Sarode ◽

Jagruti K. Save

Keyword(s):

Coordinate System ◽

Similarity Measure ◽

Efficient Method ◽

Nearest Neighbor ◽

Feature Vector ◽

Test Image ◽

Nearest Neighbor Classifier ◽

Feature Vectors ◽

The Given ◽

Neighbor Classifier

The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared withÂ distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.

Bootstrapping Distributional Feature Vector Quality

Computational Linguistics ◽

10.1162/coli.08-032-r1-06-96 ◽

2009 ◽

Vol 35 (3) ◽

pp. 435-461 ◽

Cited By ~ 11

Author(s):

Maayan Zhitomirsky-Geffet ◽

Ido Dagan

Keyword(s):

Feature Vector ◽

Similarity Measures ◽

Feature Reduction ◽

Feature Weighting ◽

Superior Performance ◽

Weighting Functions ◽

Word Similarity ◽

Feature Vectors ◽

Distributional Similarity

This article presents a novel bootstrapping approach for improving the quality of feature vector weighting in distributional word similarity. The method was motivated by attempts to utilize distributional similarity for identifying the concrete semantic relationship of lexical entailment. Our analysis revealed that a major reason for the rather loose semantic similarity obtained by distributional similarity methods is insufficient quality of the word feature vectors, caused by deficient feature weighting. This observation led to the definition of a bootstrapping scheme which yields improved feature weights, and hence higher quality feature vectors. The underlying idea of our approach is that features which are common to similar words are also most characteristic for their meanings, and thus should be promoted. This idea is realized via a bootstrapping step applied to an initial standard approximation of the similarity space. The superior performance of the bootstrapping method was assessed in two different experiments, one based on direct human gold-standard annotation and the other based on an automatically created disambiguation dataset. These results are further supported by applying a novel quantitative measurement of the quality of feature weighting functions. Improved feature weighting also allows massive feature reduction, which indicates that the most characteristic features for a word are indeed concentrated at the top ranks of its vector. Finally, experiments with three prominent similarity measures and two feature weighting functions showed that the bootstrapping scheme is robust and is independent of the original functions over which it is applied.

An Automatic Modulation Classification Network for IoT Terminal Spectrum Monitoring under Zero-sample Situations

10.21203/rs.3.rs-927160/v1 ◽

2021 ◽

Author(s):

Quan Zhou ◽

Ronghui Zhang ◽

Fangpei Zhang ◽

Xiaojun Jing

Keyword(s):

Cooperative Communication ◽

Feature Vector ◽

Recognition Accuracy ◽

Original Data ◽

Training Data ◽

Modulation Classification ◽

Spectrum Monitoring ◽

Automatic Modulation Classification ◽

Feature Vectors ◽

Wireless Signal

Abstract Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment around where we live and improve the service. The proliferation of IoT end devices has led to the misuse of spectrum resources, making spectrum regulation an important task. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. However, DL-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations firstly. Specifically, for the complexity of the original signal data, SigZSLNet generates the convolutional layer output feature vector instead of directly generating the original data of the signal. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes, and the recognition accuracy of AMC is greatly improved in zero-sample cases as a consequence. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model.

On Supervised Classification of Feature Vectors with Independent and Non-Identically Distributed Elements

Entropy ◽

10.3390/e23081045 ◽

2021 ◽

Vol 23 (8) ◽

pp. 1045

Author(s):

Farzad Shahrivari ◽

Nikola Zlatanov

Keyword(s):

Error Probability ◽

Supervised Classification ◽

Feature Vector ◽

Training Data ◽

Finite Alphabet ◽

Feature Vectors ◽

Optimal Classifier ◽

Mutually Independent ◽

Distributed Elements

In this paper, we investigate the problem of classifying feature vectors with mutually independent but non-identically distributed elements that take values from a finite alphabet set. First, we show the importance of this problem. Next, we propose a classifier and derive an analytical upper bound on its error probability. We show that the error probability moves to zero as the length of the feature vectors grows, even when there is only one training feature vector per label available. Thereby, we show that for this important problem at least one asymptotically optimal classifier exists. Finally, we provide numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of training data is small and the length of the feature vectors is sufficiently high.

Sectorization of Full Walsh Transform for Feature Vector Generation in CBIR

International Journal of Computer Theory and Engineering ◽

10.7763/ijcte.2011.v3.308 ◽

2011 ◽

pp. 217-223

Author(s):

H.B. Kekre ◽

Dhirendra Mishra

Keyword(s):

Feature Vector ◽

Walsh Transform ◽

Vector Generation

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions

Sensors ◽

10.3390/s21144638 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4638

Author(s):

Bummo Koo ◽

Jongman Kim ◽

Yejin Nam ◽

Youngho Kim

Keyword(s):

Time Series ◽

Fall Detection ◽

Discrete Data ◽

Classification Performance ◽

Training Data ◽

Processing Conditions ◽

Feature Vectors ◽

External Data ◽

Public Dataset ◽

The Cross

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training.

k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽

10.3390/math9080830 ◽

2021 ◽

Vol 9 (8) ◽

pp. 830

Author(s):

Seokho Kang

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Weighting Function ◽

High Sensitivity ◽

Training Data ◽

K Nearest Neighbor ◽

Main Challenge ◽

Benchmark Datasets ◽

Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

Maximum Variance Hashing via Column Generation

Mathematical Problems in Engineering ◽

10.1155/2013/379718 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10

Author(s):

Lei Luo ◽

Chao Zhang ◽

Yongrui Qin ◽

Chunyuan Zhang

Keyword(s):

Column Generation ◽

Large Scale ◽

Web Search ◽

Nearest Neighbor ◽

Computational Cost ◽

Multimedia Retrieval ◽

Training Data ◽

Nonlinear Dimensionality Reduction ◽

Maximum Variance ◽

Data Volume

With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search. Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing. Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work. The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data. To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions. We then extend it using anchor graphs to reduce the computational cost. Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

Manufacturing Engineering and Materials Handling, Parts A and B ◽

10.1115/imece2005-80334 ◽

2005 ◽

Cited By ~ 1

Author(s):

Guanghsu A. Chang ◽

Cheng-Chung Su ◽

John W. Priest

Keyword(s):

Artificial Intelligence ◽

Genetic Algorithms ◽

Nearest Neighbor ◽

Similarity Measures ◽

Experimental Results ◽

Case Based Reasoning ◽

Feature Weight ◽

Similar Part ◽

Case Base ◽

Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.