An Optimal Feature Selection Method for Automatic Face Retrieval Using Enhanced Grasshopper Optimization Algorithm

Facial image retrieval using its contents is one of the major areas of research because of the exponential increase of multimedia data over the Internet. However, due to high dimensional features and different variations available in the images, it becomes a challenging task to obtain the relevant and non-redundant features. Therefore, for making the facial retrieval system more accurate and computationally efficient, the selection of prominent features is an important phase. In this paper, the grasshopper optimization algorithm has been used to obtain the relevant attributes from the high dimensional features vector. For the same, Oracle Research Laboratory database of faces is used. The experimental values show the efficacy of the proposed method of feature selection which eliminates the maximum 83% features among the other considered methods and the accuracy of the facial retrieval system also increases to 91.5%.

Employing the most relevant and discriminating features is very important to achieve a successful classification with low computational cost. Although, different feature selection methods have been recently developed for this purpose, feature grouping can deal with high dimensional sparse feature vectors more effectively, yielding better interpretation of the data. In this paper, a correlation-based feature grouping (CFG) method is proposed. First, the features are grouped based on the variety of their correlation scores, and then, a new representative feature vector is generated for each group by combining its features. To investigate the strength of CFG method, two filter methods of χ2 and correlation are employed for feature selection, while classification is performed using a support vector machine (SVM) and k-Nearest Neighbor (k-NN). The empirical study on two datasets of protein-protein interactions (PPIs) and breast cancer verifies that the idea of employing feature grouping is more efficient than employing feature selection in identifying a set of features that exhibit high classification accuracy. In addition, a CFG diagram is introduced in this paper, which is used to visualize the groups and their corresponding features found by the proposed method.

In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.

Recently, Content-based medical image retrieval (CBMIR) systems enable fast diagnosis via the assessment of the visual information in medical application. Most of the state-of-the-art CBMIR systems facing few issues: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering approaches. The reasons behind this are, inability to properly handle the “semantic gap” and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields a crucial demand for developing computationally efficient and highly effective retrieval system. For this purpose, the present study proposed an efficient retrieval system which has a four-fold: First, pre-processing and Feature extraction of input image using canonical correlation analysis (CCA). By this approach extracted the feature in both pixel and feature domains and examined more rigorously. Second, applied Fuzzy C means clustering of pixel intensity values as features based on the singular value decomposition. Through this can grouping, the image based on the pixel intensity value. Third, deep convolutional neural network with SVM classifier which makes implementable and requires only a compact feature vector representation of the stored database image with their class levels during retrieval. Finally evaluated the performance based on the measure of Mean Average Precision, Correct rate (CR), Error rate (ER), Accuracy. The classification results and learned features are used for the purpose of retrieving the medical images in a database. The proposed retrieval system performs better than the traditional approach in terms of measuring average value of precision, recall, f-measure and accuracy 95.9%, 94.96%, 95.37% and 95.798% respectively. The suggested approach is best suited towards retrieving the medical images for various part of the body.

In statistical pattern recognition, it is important to estimate true distribution of patterns precisely to obtain high recognition accuracy. Normal mixtures are sometimes used for representing distributions. However, precise estimation of the parameters of normal mixtures requires a great number of sample patterns, especially for high dimensional vectors. For some pattern recognition problems, such as face recognition, very high dimensional feature vectors are necessary and there are always not enough training samples compared with the dimensionality. We present a method to estimate the distributions based on normal mixtures with small number of samples. The proposed algorithm is applied to face recognition problem which requires high dimensional feature vectors. Experimental results show the effectiveness of the proposed algorithm.

This paper is a comparative study of feature selection methods in phonotactic language recognition. The phonotactic feature is presented by n-gram statistics derived from one or more phone recognizers in the form of high dimensional feature vectors. Feature selection is necessary for its ability of reducing the dimension of feature vectors so that the higher order n-gram features can be adopted in language recognition. This paper investigates four feature selection strategies that are introduced from text categorization, including mutual information (MI), Chi-squared test (CHI), information gain (IG) and weighted log likelihood ratio (WLLR). These methods are compared on the NIST 2009 Language Recognition Evaluation (LRE) task. The experimental results show that CHI, IG and WLLR can effectively obtain much lower dimensional features without affecting the language recognition performance. In contrast, MI has relatively poor performance due to its bias towards favoring rare terms. This work is partially sup...

Position detection of elements, especially with high-precision and high-efficiency, continue to present challenges in the off-axis three-mirror space optical system to ensure the imaging quality. To test the position of elements in an off-axis three-mirror system, a method based on ResNet50 and Long short-term memory (LSTM) is proposed in this paper. In the proposed method, point spread function (PSF) with different fields of view is extracted as a high dimensional feature vector by the ResNet50 network to achieve high efficiency. The same position information in the high dimensional feature vector from different image plane is obtained by the LSTM module to achieve high precision. To evaluate the performance of the proposed method, three examples of position detection were conducted. The results indicated that the probabilities of achieving detection accuracy better than 10 µm for eccentricity and 10'' for tilt, across single-dimensional variation of components, multi-dimensional random variation, and detector variation, are 100%, 94%, and 100%, respectively. Unlike the other detection method, the positions of elements are obtained with only one calculation and are close to the final results, which saves both time and resources for adjustment. The findings indicate that the method is an accurate and effective method for detecting position of elements in off-axis three-mirror space optical system.

Rice is one of the primary foods for human, and various rice varieties have been cultivated in many countries. Automatic rice variety inspection system based on computer vision is needed instead of using technical human expert. In this chapter, we have investigated three types of local descriptor such as local binary pattern, histogram of oriented gradients and GIST to characterize rice seed images. In order to enhance the robustness, multi-view approach has been considered to concatenate these features. However, this problem encounters the high-dimensional space feature vector and need to select the relevant features for a compact and better model. We have applied different feature selection methods in a filter way to eliminate the impertinent features. The experimental results on the VNRICE dataset have been shown the efficiency of our proposition.KeywordsLBPHOGGISTFeature selectionFeature rankingMulti-view

According to the operation of the automaton transient impact, nonlinear, non-stationary signal, a method which is based on the time-frequency characteristics and PCA-SVM automaton fault diagnosis is proposed. Firstly, this paper uses statistical analysis and overall empirical mode decomposition method to construct high dimensional mixed domain initial feature vector from the characteristics of different indicators, and characterized by different description of the automaton fault analysis of different angles, then through principal component analysis, dimensionality reduction and feature extraction, the high dimensional variables changes into less dimensional independent eigenvector. At last, the principal component vectors using PCA to support vector machine, realizes the fault recognition. The experimental results show that the hybrid domain initial feature vector can accurately describe the fault characteristics, and the main feature vector extracted by PCA can discard the redundant information and simplify the classifier, and the SVM network diagnosis can improve the classification accuracy.

This paper presents algorithms to deal with problems associated with indexing high-dimensional feature vectors, which characterize video data. Indexing high-dimensional vectors is well known to be computationally expensive. Our solution is to optimally split the high dimensional vector into a few low dimensional feature vectors and querying the system for each feature vector. This involves solving an important subproblem: developing a model of retrieval which enables us to query the system efficiently. Once we formulate the retrieval problem in terms of a retrieval model, we present an optimality criterion to maximize the number of results using this model. The criterion is based on a novel idea of using the underlying probability distribution of the feature vectors. A branch-and-prune strategy optimized per each query, is developed. This uses the set of features derived from the optimality criterion. Our results show that the algorithm performs well, giving a speedup of a factor of 25 with respect to a linear search, while retaining the same level of recall.

This paper describes a retrieval system that enables a user to hum part of a melody as a query to obtain the name of a desired song from an audio database. The combined use of two musical features, tone transition and tone distribution, enhances the accuracy of the retrieval system. These features are represented as high dimensional vectors to facilitate the retrieval process. Similarity retrieval using indices of these feature vectors increases the speed of the system. This paper presents the techniques utilized in the system. Experimental results are also reported based on hummed tunes of casual singers.

Many intrusion detection systems exist, which fail to detect the intruder if rules are not coded in the system. Data mining algorithms are effective to identify the pattern of intrusion. Detection accuracy and performance of the detection engine can be considerably improved by selecting relevant features which are already proven by many researchers. A large amount of network traffic is generated by the emergence of new technologies such as cloud computing, big data. It is the responsibility of the intrusion detection system to analyze the incoming traffic. Speed and accuracy of the intrusion detection system are dependent on the total features in the dataset. However, all the features do not represent the traffic and require selection of relevant features. This feature selection is a tedious mechanism which has been proposed in the research work. Along with relevant feature selection, identification of the features which will contribute to improve the detection rate is important. Decision tree-based classifier is constructed to build suitable relevant features with recursive feature elimination process. Relevant features were identified in the dataset using the above mentioned approach with accuracy of 98%. Classifier performance can be improved by adapting a better feature selection. Better intrusion detection system could be designed by knowing the factors that help identify relevant features.

In image retrieval, relevance feedback uses information, obtained interactively from the user, to understand the user's perceptions of a query image and to improve retrieval accuracy. We propose simultaneous relevant feature selection and classification using the samples provided by the user to improve retrieval accuracy. The classifier is defined by a separating hyperplane, while the sparse weight vector characterizing the hyperplane defines a small set of relevant features. This set of relevant features is used for classification and can be used for analysis at a later stage. Mutually exclusive sets of images are shown to the user at each iteration to obtain maximum information from the user. Experimental results show that our algorithm performs better than the feature relevance weighting and feature selection schemes and comparably with the classification scheme using SVMs, in terms of retrieval accuracy, and it has the advantage of being faster than the classification scheme using SVMs.

Training SVMs on high dimensional feature vectors in one shot incurs high computational cost. A low dimensional representation reduces computational overhead and improves the classification speed. Low dimensionality also reduces the risk of over-fitting and tends to improve the generalisation ability of classification algorithms. For many important applications, the dimensionality may remain prohibitively high despite feature selection. In this paper, we address these issues primarily in the context of handwritten digit data. In particular, we make the following contributions: 1) we introduce the α-minimum feature over (α-MFC) problem and prove it to be NP-hard; 2) investigate the efficacy of a divide-and-conquer ensemble method for SVMs based on segmentation of the feature space (FS-SVMs); 3) propose a greedy algorithm for finding an approximate α-MFC using FS-SVMs.

Schizophrenia is a serious psychiatric illness which needs early and accurate diagnosis. Difference in activation patterns of schizophrenia patients and healthy subjects can be identified with the help of functional magnetic resonance imaging (fMRI). However, manual diagnosis using fMRI depends on subjective observation and may be erroneous. This has motivated the pattern recognition and machine learning research community to develop a reliable and efficient decision model for classification of schizophrenia patients and healthy subjects. However, high dimensionality and low availability of fMRI data leads to the curse-of-dimensionality problem which may degrade the performance of decision model. In the present research work, a combination of feature extraction and feature selection techniques is utilised to obtain a reduced set of relevant and non-redundant features for classification of schizophrenia patients and healthy subjects. Features are extracted from pre-processed fMRI data using a general linear model approach. Next Fisher's discriminant ratio, a univariate method, is employed for feature selection i.e. To determine a subset of discriminative features. Further, minimum Redundancy Maximum Relevance (mRMR) feature selection, a multivariate method, is employed to obtain a set of relevant and non-redundant features which are used for learning a decision model using support vector machine. Two balanced and well-age matched datasets of auditory oddball task derived from a publicly available multisite FBIRN dataset are used for experiments. First dataset consists of fMRI scans of 34 schizophrenia patients and 34 healthy subjects acquired through 1.5 Tesla scanners while second dataset consists of 25 schizophrenia patients and 25 healthy subjects acquired through 3 Tesla scanners. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches for classification of schizophrenia patients and healthy subjects using fMRI. Experimental results demonstrate that the proposed model outperforms the existing approaches in terms of sensitivity, specificity and classification accuracy. The proposed approach achieves classification accuracy of 88.2% and 78.0% for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be potential biomarkers for diagnosis of schizophrenia using fMRI.