Terms Of Feature Vectors Research Articles

Design of deep convolution feature extraction for multimedia information retrieval

PurposeThe analysis of multimedia content is being applied in various real-time computer vision applications. In multimedia content, digital images constitute a significant part. The representation of digital images interpreted by humans is subjective in nature and complex. Hence, searching for relevant images from the archives is difficult. Thus, electronic image analysis strategies have become effective tools in the process of image interpretation.Design/methodology/approachThe traditional approach used is text-based, i.e. searching images using textual annotations. It consumes time in the manual process of annotating images and is difficult to reduce the dependency in textual annotations if the archive consists of large number of samples. Therefore, content-based image retrieval (CBIR) is adopted in which the high-level visuals of images are represented in terms of feature vectors, which contain numerical values. It is a commonly used approach to understand the content of query images in retrieving relevant images. Still, the performance is less than optimal due to the presence of semantic gap among the image content representation and human visual understanding perspective because of the image content photometric, geometric variations and occlusions in search environments.FindingsThe authors proposed an image retrieval framework to generate semantic response through the feature extraction with convolution network and optimization of extracted features using adaptive moment estimation algorithm towards enhancing the retrieval performance.Originality/valueThe proposed framework is tested on Corel-1k and ImageNet datasets resulted in an accuracy of 98 and 96%, respectively, compared to the state-of-the-art approaches.

Cultural Analytics of Large Datasets from Flickr

Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to perceptual attributes is still a challenge, even more when dealing with user-generated social media content. Such images tend to be heterogenous, and using metadata-only can be misleading. This paper describes a set of tools designed to analyze large sets of user-created art related images using image features describing color, texture, composition and orientation. The proposed pipeline permits to discriminate Flickr groups in terms of feature vectors and clustering parameters. The algorithms are general enough to be applied to other domains in which the main question is about the variability of the images.

ENCODING STRUCTURAL SIMILARITY BY CROSS-COVARIANCE TENSORS FOR IMAGE CLASSIFICATION

In computer vision, an object can be modeled in two main ways: by explicitly measuring its characteristics in terms of feature vectors, and by capturing the relations which link an object with some exemplars, that is, in terms of similarities. In this paper, we propose a new similarity-based descriptor, dubbed structural similarity cross-covariance tensor (SS-CCT), where self-similarities come into play: Here the entity to be measured and the exemplar are regions of the same object, and their similarities are encoded in terms of cross-covariance matrices. These matrices are computed from a set of low-level feature vectors extracted from pairs of regions that cover the entire image. SS-CCT shares some similarities with the widely used covariance matrix descriptor, but extends its power focusing on structural similarities across multiple parts of an image, instead of capturing local similarities in a single region. The effectiveness of SS-CCT is tested on many diverse classification scenarios, considering objects and scenes on widely known benchmarks (Caltech-101, Caltech-256, PASCAL VOC 2007 and SenseCam). In all the cases, the results obtained demonstrate the superiority of our new descriptor against diverse competitors. Furthermore, we also reported an analysis on the reduced computational burden achieved by using and efficient implementation that takes advantage from the integral image representation.

Feature Extraction and Pupil Detection Algorithm Used for Iris Biometric Authentication System

Iris biometric is most mature and secure authentication system as compared to other systems. Other authentication system does exist apart from biometrics such as PIN, password etc., which are not secure and more vulnerable to attacks and can be hacked or spoofed easily. Using, Iris biometric we can enhance overall performance in terms of accuracy, which is possible if and only if the pupil part must be removed perfectly and efficiently. Pupil part is unwanted part for our system. Pupil part is surrounded by iris part when extracted successfully we get two approximate concentric circle. Proposed scanning algorithm successfully extracts pupil part and defines iris part with less complexity and more efficiently. Iris part is consisting of patterns which are desired for authenticating a person and each patterns are represented in terms of feature vectors and stored in database. Proposed system focuses on feature extraction using five level decomposition technique implemented with haar, db2 and db4 and achieves high accuracy with reduced error rates. Due to reduced errors and considering lower half of iris part, proposed algorithm can be used for larger database such as for Aadhar because it takes less time for feature extraction and has less complexity with reduced mathematical burden on the system and improves good accuracy.

SVMeFC: SVM Ensemble Fuzzy Clustering for Satellite Image Segmentation

The problem of unsupervised image segmentation of a satellite image in a number of homogeneous regions can be viewed as the task of clustering the pixels in the intensity space. This letter presents an approach that exploits the capability of some recently proposed fuzzy clustering techniques, as well as support vector machine (SVM) classifiers, to yield improved solutions. All the fuzzy clustering techniques are first used to produce a set of different clustering solutions. Each such solution has been improved by a novel technique based on an SVM classifier. Thereafter, the cluster-based similarity partition algorithm is used to create the final clustering solution from all improved ensemble solutions. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, a remotely sensed image of Calcutta City has been segmented using the proposed technique to establish its utility. In addition, the additional information of this letter is given as supplementary at http://sysbio.icm.edu.pl/indra/SVMeFC.html.

An Adaptive Masker for the Differential Evolution Algorithm

The automatic clustering problem of a large and complex data set into different homogeneous clusters is a challenging task. The choose of a variable length clusters centers using a good method to generate the maskers is an important phase in the evolution of a global search heuristics algorithms. In this paper, a new technique of real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically detects the optimal number of clusters and performs the proper partitioning from a data set. The effectiveness of the proposed technique is demonstrated by comparing it with other popular clustering algorithms. In the comparative study, a remote sensing data described in terms of feature vectors and a remote sensing image are used for the automatic partitioning and classifying tasks.

A novel semisupervised SVM for pixel classification of remote sensing imagery

This article introduces a semisupervised support vector machine classification technique that exploits both labeled and unlabeled points for addressing the problem of pixel classification of remote sensing images. The proposed technique is based on applying the margin maximization principle to both labeled and unlabeled patterns. Semisupervised SVM progressively searches a reliable discriminant hyperplane in the high dimensional space through iterative method exploiting both labeled and unlabeled samples. In particular, the dynamic thresholding and successive filtering of the unlabeled set are exploited to find a reliable separating hyperplane. The proposed technique is first demonstrated for six labeled datasets described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery and compared with the standard SVM. Experimental results confirm that employing this learning scheme removes unnecessary points to a great extent from the unlabeled set and increases the accuracy level on the other hand. Comparison is made in terms of accuracy, ROC, AUC and F-measure for the labeled data and quantitative cluster validity indices as well as classified image quality for the image data.

Efficient parallel algorithm for pixel classification in remote sensing imagery

An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently developed point symmetry-based distance norm is proposed. The proposed distributed computing time efficient point symmetry based K-Means technique is able to correctly identify presence of overlapping clusters of any arbitrary shape and size, whether they are intra-symmetrical or inter-symmetrical in nature. A Kd-tree based approximate nearest neighbor searching technique is used as a speedup strategy for computing the point symmetry based distance. Superiority of this new parallel implementation with the novel two-phase speedup strategy over existing parallel K-Means clustering algorithm, is demonstrated both quantitatively and in computing time, on two SPOT and Indian Remote Sensing satellite images, as even K-Means algorithm fails to detect the symmetry in clusters. Different land cover regions, classified by the algorithms for both images, are also compared with the available ground truth information. The statistical analysis is also performed to establish its significance to classify both satellite images and numeric remote sensing data sets, described in terms of feature vectors.

A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery

In this article, we present a semisupervised support vector machine that uses self-training approach. We then construct an ensemble of semisupervised SVM classifiers to address the problem of pixel classification of remote sensing images. Semisupervised support vector machines (S^3VMs) are based on applying the margin maximization principle to both labeled and unlabeled samples. The ensemble of SVM classifiers recognizes the conceptual similarity between component classifiers from the same data source. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on these datasets show that employing this learning scheme can increase the accuracy level. The performance of the ensemble is compared with one of its component classifier and conventional SVM in terms of accuracy and quantitative cluster validity indices.

Automatic Fuzzy Clustering Using Modified Differential Evolution for Image Classification

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types, some of which cover significantly large areas while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, a new real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Here, the assignment of points to different clusters is done based on a Xie-Beni index where the Euclidean distance is taken into consideration. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then in identifying different landcover regions in remote sensing imagery. The superiority of the new method is demonstrated by comparing it with other existing techniques like automatic clustering using improved differential evolution, classical differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering, and well known fuzzy C-means algorithm both qualitatively and quantitatively.

A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images

Satellite image classification is a complex process that may be affected by many factors. This article addresses the problem of pixel classification of satellite images by a robust multiple classifier system that combines k-NN, support vector machine (SVM) and incremental learning algorithm (IL). The effectiveness of this combination is investigated for satellite imagery which usually have overlapping class boundaries. These classifiers are initially designed using a small set of labeled points. Combination of these algorithms has been done based on majority voting rule. The effectiveness of the proposed technique is first demonstrated for a numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on numeric data as well as two remote sensing data show that employing combination of classifiers can effectively increase the accuracy label. Comparison is made with each of these single classifiers in terms of kappa value, accuracy, cluster quality indices and visual quality of the classified images.

GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING

Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propose an approach transforming graphs into n-dimensional real vectors by means of prototype selection and graph edit distance computation. This approach allows one to build graph kernels in a straightforward way. It is not only applicable to graphs, but also to other kind of symbolic data in conjunction with any kind of dissimilarity measure. Thus it is characterized by a high degree of flexibility. With several experimental results, we prove the robustness and flexibility of our new method and show that our approach outperforms other graph classification methods on several graph data sets of diverse nature.

Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective Fuzzy Clustering Combined With SVM Classifier

The problem of unsupervised classification of a satellite image in a number of homogeneous regions can be viewed as the task of clustering the pixels in the intensity space. This paper proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM) classifier to yield improved solutions. The multiobjective technique is first used to produce a set of nondominated solutions. The nondominated set is then used to find some high-confidence points using a fuzzy voting technique. The SVM classifier is thereafter trained by these high-confidence points. Finally, the remaining points are classified using the trained classifier. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Moreover, two remotely sensed images of Bombay and Calcutta cities have been classified using the proposed technique to establish its utility.

Fuzzy relational clustering based on comparing two proximity matrices with utilization of particle swarm optimization

The first stage of knowledge acquisition and reduction of complexity concerning a group of entities is to partition or divide the entities into groups or clusters based on their attributes or characteristics. Clustering algorithms normally require both a method of measuring proximity between patterns and prototypes and a method for aggregating patterns. However sometimes feature vectors or patterns may not be available for objects and only the proximities between the objects are known. Even if feature vectors are available some of the features may not be numeric and it may not be possible to find a satisfactory method of aggregating patterns for the purpose of determining prototypes. Clustering of objects however can be performed on the basis of data describing the objects in terms of feature vectors or on the basis of relational data. The relational data is in terms of proximities between objects. Clustering of objects on the basis of relational data rather than individual object data is called relational clustering. The premise of this paper is that the proximities between the membership vectors, which are obtained as the objective of clustering, should be proportional to the proximities between the objects. The values of the components of the membership vector corresponding to an object are the membership degrees of the object in the various clusters. The membership vector is just a type of feature vector. Based on this premise, this paper describes another fuzzy relational clustering method for finding a fuzzy membership matrix. The method involves solving a rather challenging optimization problem, since the objective function has many local minima. This makes the use of a global optimization method such as particle swarm optimization (PSO) attractive for determining the membership matrix for the clustering. To minimize computational effort, a Bayesian stopping criterion is used in combination with a multi-start strategy for the PSO. Other relational clustering methods generally find local optimum of their objective function.

Multiobjective Genetic Clustering for Pixel Classification in Remote Sensing Imagery

An important approach for unsupervised landcover classification in remote sensing images is the clustering of pixels in the spectral domain into several fuzzy partitions. In this paper, a multiobjective optimization algorithm is utilized to tackle the problem of fuzzy partitioning where a number of fuzzy cluster validity indexes are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centers is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency

Self-organizing map for clustering in the graph domain

Self-organizing map (som) is a flexible method that can be applied to various tasks in pattern recognition. However it is limited in the sense that it uses only pattern representations in terms of feature vectors. It was only recently that an extension to strings was proposed. In the present paper we go a step further and present a version of som that works in the domain of graphs. Graphs are a powerful data structure that include pattern representations based on strings and feature vectors as special cases. After introducing the new method a number of experiments will be described demonstrating its feasibility in the context of a graph clustering task.