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Clothing retrieval is very useful to help the clients to efficiently search out the apparel they want. Currently, the mainstream clothing retrieval methods are attribute semantics based, which however are inconvenient for common clients. The purpose of this paper is to provide an easy‐to‐operate apparels retrieval mode with the authors' novel approach of clothing image similarity measurement.

The authors measure the similarity between two clothing images by computing the weighted similarities between their bundled features. Each bundled feature consists of the point features (SIFT) which are further quantified into local visual words in a maximally stable extremal region (MSER). The authors weight the importance of bundled features by the precision of SIFT quantification and local word frequency that reflects the frequency of the common visual words appeared in two bundled features. The bundled features similarity is computed from two aspects: local word frequency; and SIFTs distance matrix that records the distances between every two SIFTs in a bundled feature.

Local word frequencies improves the recognition between two bundled features with the same common visual words but different local word frequency. SIFTs distance matrix has the merits of scale invariance and rotation invariance. Experimental results show that this approach works well in the situations with large clothing deformation, background exchange and part hidden, etc. And the similarity measurement of Weight+Bundled+LWF+SDM is the best.

This paper presents an apparel retrieval mode based on local visual features, and presents a new algorithm for bundled feature matching and apparel similarity measurement.

The purpose of this paper is to examine the role of perceived external image and similarity in values, beliefs and interests with an organization's leaders and other members on organizational identification.

The paper presents results of a field survey research in two non‐work organizational contexts, a professional association, and a college business fraternity. Hypotheses were tested with ordinary least squares regression and mediation analyses.

Perceived external image and perceived similarity with the organization's leaders and other members influence organizational identification. Perceived similarity partially mediates the relationship between external image and identification.

The study implements a cross‐sectional design and relies on self‐reports. The results have important implications for organizational identification and related behaviors both in work and non‐work contexts.

The study presents implications for enhancing member identification with an organization, which is related to increased involvement and continued membership. A positive external image may increase the likelihood that organizational members internalize values, beliefs and interests held by the organization's leaders and other members.

The study is based on a model of identity orientation that differentiates across personal, relational, and collective orientations. It measures perceived similarity with social referents in values, beliefs and interests, and study traditionally overlooked non‐work contexts.

The purpose of this paper is to demonstrate the effectiveness and advantages of using perceptual tolerance neighbourhoods in tolerance space‐based image similarity measures and its application in content‐based image classification and retrieval.

The proposed method in this paper is based on a set‐theoretic approach, where an image is viewed as a set of local visual elements. The method also includes a tolerance relation that detects the similarity between pairs of elements, if the difference between corresponding feature vectors is less than a threshold 2 (0,1).

It is shown that tolerance space‐based methods can be successfully used in a complete content‐based image retrieval (CBIR) system. Also, it is shown that perceptual tolerance neighbourhoods can replace tolerance classes in CBIR, resulting in more accuracy and less computations.

The main contribution of this paper is the introduction of perceptual tolerance neighbourhoods instead of tolerance classes in a new form of the Henry‐Peters tolerance‐based nearness measure (tNM) and a new neighbourhood‐based tolerance‐covering nearness measure (tcNM). Moreover, this paper presents a side – by – side comparison of the tolerance space based methods with other published methods on a test dataset of images.

This paper aims to propose a system to acquire images automatically for digital reconstruction of heritage artifacts using a six-degree of freedom industrial manipulator.

A virtual environment is created using Robot Studio® software to integrate the trajectory and differential motion of the robot manipulator and the motion of camera while acquiring images. A new area similarity matrix method is proposed to reduce the number of images required for digital reconstruction using Autodesk Recap® software. Real-time experiments have been performed using objects such as minion, ultimaker robot and cube. Evaluation of the digital reconstruction is conducted using the contour area matching method.

The number of images required for reconstruction based on area similarity matrix method is reduced to 63 per cent when compared with the random selection method. Quality parameters such as surface area, volume, number of defect holes, vertices and faces are enhanced for the proposed method.

Digital reconstruction of large-sized heritage artifacts cannot be performed in this setup. But this can be overcome by fixing the manipulator on a mobile platform or overhead crane. This paper does not discuss the reconstruction of partially damaged heritage artifacts, which could be accomplished based on deep learning techniques.

Using this approach, off-the-shelf heritage artifacts and large-scale objects can be reconstructed digitally with a minimum number of images and without compromising the quality of original models.

To the best of the authors’ knowledge, area similarity-based approach in 3D digital reconstruction by coupling the kinematics of an industrial manipulator and camera is proposed for the first time. A fully automated digital reconstruction technology to preserve valuable heritage artifacts has been developed. It also highlights the space constraints of the industrial manipulator in digital reconstruction.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene.

In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity.

Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures.

The paper presents an effective retrieval method for hyperspectral images.

Image‐based localisation has been widely investigated in mobile robotics. However, traditional image‐based localisation approaches do not work when the environment appearance changes. The purpose of this paper is to propose a new system for image‐based localisation, which enables the approach to work also in highly dynamic environments.

The proposed technique is based on the use of a distributed vision system (DVS) composed of a set of cameras installed in the environment and of a camera mounted on a mobile robot. The localisation of the robot is achieved by comparing the current image grabbed by the robot with the images grabbed, at the same time, by the DVS. Finding the DVS's image, most similar to the robot's image, gives a topological localisation of the robot.

Experiments reported in the paper proved the system to be effective, even exploiting a pre‐existent DVS not designed for this application.

Whilst, aware that DVSs, as the one used in this work, are not diffuse nowadays, this work is significant because a novel idea is proposed for dealing with dynamic environments in the image‐based localisation approach and the idea is validated with experiments. Camera Sensor networks currently are an emerging technology and they may be introduced in several daily environments in the future.

Consumers' evaluations of brand extensions have gained considerable attention in the marketing literature. The purpose of this study is to investigate how a brand's perceived global or local origin impacts evaluations of brand extensions and creates transfer effects of brand meaning. The paper conceptually characterizes the transference process and empirically tests the nature and extent of spillover effects of origin associations across multiple parent brands and extensions.

For the empirical testing of the conceptual model of transfer effects of origin associations we undertook a cross‐sectional consumer survey amongst a sample of 267 Norwegian respondents. Structural equation modelling was used to investigate the causal relationships between the latent exogenous and endogenous variables in the conceptual model.

The present study indicates that the global and local origin framework, first introduced by Steenkamp et al. in 2003, can explain the occurrence of reciprocal transfer of brand meaning across parent brands and extensions. The paper shows that global and local origin associations operate in a manner very similar to brand associations in the transference of perceptions. It finds that distinct origin associations influence the pre‐brand image and drive the forward effect on the attitude towards the extension as well as the subsequent backward effect upon the post‐brand image of the parent brand.

This paper reveals for the first time that distinct origin associations can initiate spillover effects across parent brands and extensions. This study is therefore an important step towards the generalizability of main brand extension studies to other contexts such as extensions of global brands.

This paper aims to present a novel feature design that is able to precisely describe salient objects in images. With the development of space survey, sensor and information acquisition technologies, more complex objects appear in high-resolution remote sensing images. Traditional visual features are no longer precise enough to describe the images.

A novel remote sensing image retrieval method based on VSP (visual salient point) features is proposed in this paper. A key point detector and descriptor are used to extract the critical features and their descriptors in remote sensing images. A visual attention model is adopted to calculate the saliency map of the images, separating the salient regions from the background in the images. The key points in the salient regions are then extracted and defined as VSPs. The VSP features can then be constructed. The similarity between images is measured using the VSP features.

According to the experiment results, compared with traditional visual features, VSP features are more precise and stable in representing diverse remote sensing images. The proposed method performs better than the traditional methods in image retrieval precision.

This paper presents a novel remote sensing image retrieval method based on VSP features.

The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR).

The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate).

The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|R_D) is able to erase the negative influences of error images in R_D.

Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach.

Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.