High-dimensional Image Datasets Research Articles

Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods

Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex datasets but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image datasets, such as Persian handwritten images, have numerous pixels, making statistical inference difficult. Such high-dimensionality property pose challenges for analysis and processing, requiring specialized techniques like clustering to extract information. Incorporating response variable information enhances clustering analysis, transforming it into a supervised method. This article evaluates a supervised clustering approach using Ridge and Lasso penalties, comparing them in analyzing a real dataset while identifying important variables. We demonstrate that despite choosing a small number of variables as important variables, Lasso penalty performs relatively well in predicting the labels of new observations for this multi-class dataset.

Revisiting the performance of PCA versus FDA versus Simple Projection for image recognition

In this paper, our main aim is to show a better dimension reduction process of high dimensional image data sets from several existing techniques. To verify it we start with most useful singular value decomposition to reduce the dimensionality of data to incorporate principal components. On the other hand, we classify data in advance to work out Fisher’s discriminant. From many real-world examples, we set a very well-known paradigm of analysis using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Fisher Discriminant Analysis (FDA) and Simple Projection (SP) to recognize people from their facial images. We consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images). Moreover, we show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three different machine learning techniques.

Revisiting the Performance of PCA Versus FDA Versus Simple Projection for Image Recognition

Computer vision becomes a great area of research due to the huge availability of images and videos. For enhancement of security, biomedical imaging, or automation of identification, one may need useful tools to recognize images. One main problem of image data set is high dimensional, and it is very expensive to work with huge dimensions. In this paper, our main aim is to show a better dimension reduction process of high dimensional image data sets from several existing techniques. To verify it we start with most useful singular value decomposition to reduce the dimensionality of data to incorporate principal components. On the other hand, we classify data in advance to work out Fisher’s discriminant. From many real-world examples, we set a very well-known paradigm of analysis using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) or Fisher Discriminant Analysis (FDA) and Simple Projection (SP) to recognize people from their facial images. We consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images). Moreover, we show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three different machine learning techniques.

A fast and scalable similarity search in high-dimensional image datasets

Owing to the development of image data production and use, the quantity of image datasets has exponentially increased in the last decade. Consequently, the similarity searching cost in image datasets becomes a severe problem which affects the efficiency of similarity search engines in this data type. In this paper, we address the problem of reducing the similarity search cost in large, high-dimensional and scalable image datasets; we propose an improvement of the D-index method to reduce the searching cost and to deal efficiently with scalable datasets. The proposed improvement is based on two propositions; first, we propose criteria and algorithms to choose effective separation values which can reduce the searching cost. Second, we propose an algorithm for updating the structure in case of scalable datasets to resist the impact of objects' insertion on the searching cost. The experiments show that the proposed D-index version has proved a good searching performance in comparison with the classical D-index and a significant resistance to the dataset scalability against the original D-index.

A fast and scalable similarity search in high-dimensional image datasets

A fast and scalable similarity search in high-dimensional image datasets

Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection

Abstract Image outlier detection has been an important research issue for many computer vision tasks. However, most existing outlier detection methods fail in the high-dimensional image datasets. In order to address this problem, we propose a novel image outlier detection method by combining autoencoder with Adaboost (ADAE). By ensembling many weak autoencoders, our method can better capture the statistical correlations among the features of normal data than the single autoencoder. Therefore, the proposed ADAE is able to determine the outliers efficiently. In order to reduce the many parameters in ADAE, we introduce the Sparse Group Lasso (SGL) constraint into the learning objective of ADAE. We combine Adagrad with Proximal Gradient Descent to optimize this additional learning objective. We also propose the multi-objective evolutionary algorithm to determine the best penalty factors of SGL. By evaluating on several famous image datasets, the detection results testify to the outstanding outlier detection performance of ADAE. The evaluation results also show SGL can make the detection model more compact while maintaining the similar detection performance.

Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering

Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.

Intrinsic dimension estimation of manifolds by incising balls

Dimensionality reduction is a very important tool in data mining. Intrinsic dimension of data sets is a key parameter for dimensionality reduction. However, finding the correct intrinsic dimension is a challenging task. In this paper, a new intrinsic dimension estimation method is presented. The estimator is derived by finding the exponential relationship between the radius of an incising ball and the number of samples included in the ball. The method is compared with the previous dimension estimation methods. Experiments have been conducted on synthetic and high dimensional image data sets and on data sets of the Santa Fe time series competition, and the results show that the new method is accurate and robust.

The GC-tree: a high-dimensional index structure for similarity search in image databases

We propose a new dynamic index structure called the GC-tree (or the grid cell tree) for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for a clustered high-dimensional image dataset. The basic ideas are threefold: 1) we adaptively partition the data space based on a density function that identifies dense and sparse regions in a data space; 2) we concentrate the partition on the dense regions, and the objects in the sparse regions of a certain partition level are treated as if they lie within a single region; and 3) we dynamically construct an index structure that corresponds to the space partition hierarchy. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional image datasets. To demonstrate the practical effectiveness of the GC-tree, we experimentally compared the GC-tree with the IQ-tree, LPC-file, VA-file, and linear scan. The result of our experiments shows that the GC-tree outperforms all other methods.