Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

As one of the classical nonlinear dimensionality reduction algorithms, Locally Linear Embedding (LLE) has shown powerful performance in many research fields. However, there are still two limitations in LLE: (1) traditional LLE is sensitive to high-curvature noise; (2) the computation is too expensive. To solve these problems, we present Quasi-curvature LLE (QLLE) through taking the curvature of local neighborhoods into consideration when mapping local configuration into low-dimensional coordinates. And then a novel learning framework called Quasi-curvature Local Linear Projection (QLLP) is proposed for efficient dimensionality reduction. This framework first selects small landmarks from original data to obtain the low-dimensional coordinates in QLLE, and then adopts Extreme Learning Machine (ELM) to learn the explicit mapping function from original data to low-dimensional coordinates for nonlinear dimensionality reduction. The extensive experiments in synthetic and Frey facial expression datasets demonstrate that this framework can greatly improve the efficiency in nonlinear dimensionality reduction.

Human action segmentation is important for human action analysis, which is a highly active research area. Most segmentation methods are based on clustering or numerical descriptors, which are only related to data, and consider no relationship between the data and physical characteristics of human actions. Physical characteristics of human motions are those that can be directly perceived by human beings, such as speed, acceleration, continuity, and so on, which are quite helpful in detecting human motion segment points. We propose a new physical-based descriptor of human action by curvature sequence warp space alignment (CSWSA) approach for sequence segmentation in this paper. Furthermore, time series-warp metric curvature segmentation method is constructed by the proposed descriptor and CSWSA. In our segmentation method, descriptor can express the changes of human actions, and CSWSA is an auxiliary method to give suggestions for segmentation. The experimental results show that our segmentation method is effective in both CMU human motion and video-based data sets.

Incompatibility of image descriptor and ranking has been often neglected in image retrieval. In this paper, Manifold Learning and Gestalt Psychology Theory are involved to solve the problem of incompatibility. A new holistic descriptor called Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed, which combines color and gradient direction to imitate human visual uniformity. PUD features in the same class images distributes on one manifold in most cases, as PUD improves the visual uniformity of the traditional descriptors. Thus, we use manifold ranking and PUD to realize image retrieval. Experiments were carried out on four benchmark data sets, and the proposed method is shown to greatly improve the accuracy of image retrieval. Our experimental results in Ukbench and Corel-1K datasets demonstrate that N-S score reached 3.58 (HSV 3.4) and mAP at 81.77% (ODBTC 77.9%) respectively by utilizing PUD which has only 280 dimensions. The results are higher than other holistic image descriptors including local ones as well as state-of-the-arts retrieval methods.

As the significant component in Industrial Internet of Things (lIoT), sensor networks have been applied widely in many fields. However, concept drift in data stream produced in sensor networks always brings great difficulty for the robustness of data processing. To solve the problem, we propose a novel concept drift detection method based on angle optimized global embedding (AOGE) and principal component analysis (PCA) for data stream learning in sensors networks. AOGE and PCA analyze the principal components through the projection variance and the projection angle in the subspace, respectively. And then the occurrence of concept drift is determined by observing the change of subspace for each data stream patch. The experiments in synthetic datasets and Intel Lab data demonstrate witness the effectiveness of our method. (C) 2016 Published by Elsevier Ltd.

Relevance feedback (RF) has long been an important approach for multi-media retrieval because of the semantic gap in image content, where SVM based methods are widely applied to RF of content-based image retrieval. However, RF based on SVM still has some limitations: (1) the high dimension of image features always make the RF time-consuming; (2) the model of SVM is not discriminative, because labels of image features are not sufficiently exploited. To solve above problems, we proposed robust discriminative extreme learning machine (RDELM) in this paper. RDELM involved both robust within-class and between-class scatter matrices to enhance the discrimination capacity of ELM for RF. Furthermore, an angle criterion dimensionality reduction method is utilized to extract the discriminative information for RDELM. Experimental results on four benchmark datasets (Corel-1K, Corel-5K, Corel-10K and MSRC) illustrate that our proposed RF method in this paper achieves better performance than several state-of-the-art methods.

Image representation and ranking are crucial parts in image retrieval. These two steps are independently constructed in most retrieval models, but the compatibility between descriptors and ranking algorithms play an important role. Inspired by human vision perception and manifold learning, we propose a novel image retrieval framework in this paper. We first propose an image representation called texton uniform descriptor, and then illustrate the preservation of the intrinsic manifold structure through visualizing the distribution of image representations on the two-dimensional manifold. This characteristic provides the foundation for subsequent manifold-based ranking. To further improve the efficiency in image retrieval, we propose modified manifold ranking (MMR) which aims at selecting small-scale images randomly as landmarks to propagate adjacent similarity among images iteratively. The extensive experiments in four public datasets demonstrate that our framework has better performance than other state-of-the-art methods in image retrieval. (c) 2017 Published by Elsevier Inc.

A novel feature extraction method called three-structure descriptor (TSD) used in HSV color space is proposed to address the problem that the image description methods based on texton pay undue attention to the color description so reduce the performance of image retrieval due to directly extracting other features in color space.The proposed method extracts color and texture information in HSV color space and the same importance of both the color and texture is taken into account while the excessive interference of color information is avoided.TSD uses the information change among pixels to represent the local spatial structure information in texture feature extraction,which solves the problem that the traditional local pattern methods disregard the spatial correlation in local structure and captures more information regarding to spatial structure information.Experimental results show that the retrieval precisions of the proposed method reach 78.08％,38.12％ and 52.12％ on the Corel-1000,Corel-5000 and Corel-10000 databases respectively,and its retrieval precisions are higher than those of existing methods based on texton.

Convolutional Neural Network(CNN) is a kind of deep learning and it has become a current hot topic in the field of image recognition. In the CNN, Output layer consists of Euclidean Radial Basis Function, unit matrix column as CNN's label vector. The category of the input image can be interpreted as the nearest label vector. This paper addresses a question: what is CNN's optimal label vector? We show that label vector influence CNN's accuracy. Most surprisingly, we show that the new label vector achieves the lowest known error rate on the Convolutional net LeNet-5, unprocessed MNIST dataset (0.45%).

In this paper, a novel image descriptor, called Color Binary Correlation (CBC), is proposed for image retrieval. This method defines and describes the structure elements utilizing binary patterns based on colors and edge orientation respectively, and thus integrate texture with the other two properties. Besides, its variants CBCri and CBCu2, which are presented for rotated invariance and "unform" patterns respectively, are presented and analyzed for image retrieval. The experimental results in Corel-1K and Corel-10K datasets demonstrate that our descriptors are more robust and discriminative than other methods and CBC shows better performance than CBCri and CBCu2 because of taking more possible patterns into consideration.

To solve the problem that conventional texton methods do not describe the similarity information among the surrounding neighbors for a given center pixel in different color quantization levels,a feature descriptor,called semicircle local binary patterns structure correlation descriptor (SLBPSCD)is proposed,which is applied for image retrieval.Firstly,a novel semicircle local binary patterns structure texton is defined.Secondly,the structure textons are detected in different color quantization levels.Finally,the spatial distribution and contrast features of new structure texton are extracted.The proposed descriptor gets much more discriminative structure than conventional texton-based methods by taking more structure texton into consideration.Experimental results for different image databases verify the effectiveness of the proposed method.

The performance of content‐based image retrieval (CBIR) depends to a great extent on the image feature descriptor .Among these descriptors ,color difference histogram (CDH) has showed the great discriminative performance in CBIR .However ,there are still some limitations in it :1)only taking color difference of pixels in global region into account ;2)not considering the spatial structure among pixels .In this paper ,to solve these problems ,we propose a novel image representation ,called texton correlation descriptor (TCD) ,which is applied to CBIR .First ,we define uniform regions w hich contain discriminative information of images and then detect them by analyzing the relationship among low‐level features (color value and local binary patterns ) of pixels . Second , in order to character contrast and spatial structure information in uniform regions respectively ,we propose the color difference feature w hich fuses color difference correlation and global color difference histogram , and texton frequency feature which fuses texton frequency correlation and texton frequency histogram .Finally , by combining these feature vectors , TCD not only characters two orthogonal properties :spatial structure and contrast ,but also takes these properties in local and global uniform regions into account simultaneously so that TCD has better performance in CBIR .The experimental results show that the retrieval results of TCD is higher than that of other descriptors in image datasets ,and thus demonstrate that TCD is more robust and discriminative in CBIR .

Incompatibility of image descriptor and ranking is always neglected in image retrieval. In this paper, manifold learning and Gestalt psychology theory are involved to solve the incompatibility problem. A new holistic descriptor called Perceptual Uniform Descriptor (PUD) based on Gestalt psychology is proposed, which combines color and gradient direction to imitate the human visual uniformity. PUD features in the same class images distributes on one manifold in most cases because PUD improves the visual uniformity of the traditional descriptors. Thus, we use manifold ranking and PUD to realize image retrieval. Experiments were carried out on five benchmark data sets, and the proposed method can greatly improve the accuracy of image retrieval. Our experimental results in the Ukbench and Corel-1K datasets demonstrated that N-S score reached to 3.58 (HSV 3.4) and mAP to 81.77% (ODBTC 77.9%) respectively by utilizing PUD which has only 280 dimension. The results are higher than other holistic image descriptors (even some local ones) and state-of-the-arts retrieval methods.

With the applications heterogeneous of Internet of Things (IoT) technology, the heterogeneous IoT systems generate a large number of heterogeneous datas, including videos and images. How to efficiently represent these images is an important and challenging task. As a local descriptor, the texton analysis has attracted wide attentions in the field of image processing. A variety of texton-based methods have been proposed in the past few years, which have achieved excellent performance. But, there still exists some problems to be solved, especially, it is difficult to describe the images with complex scenes from IoT. To address this problem, this paper proposes a multi-feature representation method called diagonal structure descriptor. It is more suitable for intermediate feature extraction and conducive to multi-feature fusion. Based on visual attention mechanism, five kinds of diagonal structure textons are defined by the color differences of diagonal pixels. Then, four types of visual features are extracted from the mapping sub-graphs and integrated into 1-D vector. Various experiments on three Corel-datasets demonstrate that the proposed method performs better than several state-of-the-art methods.

The image descriptors based on multi-features fusion have better performance than that based on simple feature in content-based image retrieval (CBIR). However, these methods still have some limitations: (1) the methods that define directly texture in color space put more emphasis on color than texture feature; (2) traditional descriptors based on histogram statistics disregard the spatial correlation between structure elements; (3) the descriptors based on structure element correlation (SEC) disregard the occurring probability of structure elements. To solve these problems, we propose a novel image descriptor, called Global Correlation Descriptor (GCD), to extract color and texture feature respectively so that these features have the same effect in CBIR. In addition, we propose Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) which can integrate the advantages of histogram statistics and SEC to characterize color and texture features respectively. Experimental results demonstrate that GCD is more robust and discriminative than other image descriptors in CBIR. (C) 2015 Elsevier Inc. All rights reserved.

This paper presents a new relevance feedback scheme, which incorporates Extreme Learning Machine (ELM) to content-based image retrieval (CBIR) with relevance feedback. Relevance feedback schemes based on Support Vector Machine (SVM) have been proposed in previous paper. However, the performance of the schemes are often poor which is caused by the low speed of SVM algorithm in high dimension data. To overcome the problem, ELM is applied to construct a classifier for relevance feedback instead of Support Vector Machine (SVM) which has been used in CBIR. Due to the faster speed and the higher accuracy of ELM algorithm, we can achieve better performance with the proposed scheme in image retrieval. Our experiments also show that it is feasible to incorporate ELM with relevance feedback for CBIR.