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Content‐based image retrieval suffers from the semantic gap problem: that images are represented by low‐level visual features, which are difficult to directly match to high‐level concepts in the user's mind during retrieval. To date, visual feature representation is still limited in its ability to represent semantic image content accurately. This paper seeks to address these issues.

In this paper the authors propose a novel meta‐feature feature representation method for scenery image retrieval. In particular some class‐specific distances (namely meta‐features) between low‐level image features are measured. For example the distance between an image and its class centre, and the distances between the image and its nearest and farthest images in the same class, etc.

Three experiments based on 190 concrete, 130 abstract, and 610 categories in the Corel dataset show that the meta‐features extracted from both global and local visual features significantly outperform the original visual features in terms of mean average precision.

Compared with traditional local and global low‐level features, the proposed meta‐features have higher discriminative power for distinguishing a large number of conceptual categories for scenery image retrieval. In addition the meta‐features can be directly applied to other image descriptors, such as bag‐of‐words and contextual features.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This paper applies the mixed logit and the latent class models to analyse the heterogeneity in foreign investment location choices in Central and Eastern Europe. The empirical results show that the responsiveness of the probabilities of choices to invest in a particular location to country-level variables differs both across sectors and across firms of different characteristics. The paper highlights the superiority of the latent class model with regards to the model fit and the interpretation of results.

The purpose of this paper is to offer a new gender- and class-sensitive framework for research on rural women entrepreneurship by focusing on the women’s agricultural cooperatives in Turkey. Although these cooperatives have been promoted as ideal bottom-to-top organizations to integrate women into economy as entrepreneurs, there has been significant decline in their numbers. This paper tackles with this contradictory situation and intends to offer an alternative research framework on the viability of the women’s agricultural cooperatives in Turkey.

The paper is built on a critical assessment of the existing literature. It argues that a framework that brings together macro-, meso- and micro-factors will provide a springboard to unfold the gendered processes integral to rural female entrepreneurship in Turkey. Drawing on intersectional theory, the multilayered factors which operate to rural women’s (dis)advantages through the cooperatives are unfolded as policymaking, policy implementation and everyday experiences.

For policymakers and implementers, it points out the need for a holistic and integrated understanding of rural female entrepreneurship and for re-formulation of policies at the state level. For rural women, it draws attention to the measures required to be taken at the cooperative level to overcome inequalities.

This paper is original in making explicit social, political and economic embeddedness of female entrepreneurship in rural Turkey.

Purpose – This chapter reflects on the role of cycling in India, Sub-Saharan Africa and Latin America, discusses and compares explanatory factors of cycling behaviour and provides three methods of spatial analysis that can feed into local transport policy and planning.

Approach – The chapter compares important relevant contextual issues and challenges and presents examples of ongoing research on three continents.

Findings – The findings are in the first instance methodological in nature. Methods have been developed to assess the effect of barriers on access by bicycle, to quantify the avoided carbon emission associated with cycling and to help plan a demand-based cycling network.

Practical implications – Three different spatial analysis methods are presented: the planning of new bicycle infrastructure, the evaluation of existing cycling in terms of avoided carbon emission and the role of the physical environment in levels of cycling accessibility. The methods can be easily replicated and integrated into transport policy and planning at the local level.

Social implications – Effective cycling-inclusive planning in developing countries is expected to lead to higher levels of cycling that positively affect people's welfare, health and the environment.

Value of chapter – The chapter affirms that a thorough understanding of physical, social, economic and cultural factors of the developing city context are important in effective cycling-inclusive planning. It provides three relatively simple and replicable methods that are considered particularly appropriate for data scarce developing cities.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images.

The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results.

The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved.

This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

This paper aims to present recently published resources on information literacy and library instruction through an extensive annotated bibliography of publications covering all library types.

This paper annotates English-language periodical articles, monographs, dissertations and other materials on library instruction and information literacy published in 2017 in over 200 journals, magazines, books and other sources.

The paper provides a brief description for all 590 sources.

The information may be used by librarians and interested parties as a quick reference to literature on library instruction and information literacy.