Due to the large diversity of existing feature descriptors in content-based image retrieval, the image contents can be better represented by the joint use of several descriptors in order to explore their potentially complementary characteristics. This paper presents and discusses a strategy for fusion of the different multidimensional features involved, based on inverted multi-indices and dedicated to similarity search. Image descriptors are quantized separately and efficiently through dimension reduction techniques, before being combined in the inverted multi-indices. To exhibit its effectiveness, the proposal is evaluated on two datasets having different contents and sizes, facing several state-of-the-art approaches of image descriptor fusion. The obtained results reconfirm that the joint use of several descriptions improves similarity search, and show that our fusion proposal outperforms other solutions, while manipulating lower or similar volumes of features.

This article addresses the integration of cultural perspectives in the smart city discourse and in the implementation of the UN Agenda 2030; it does so specifically with respect to land patterns and land use. We hope to increase the ability of relevant stakeholders, including scientific communities working in that field, to handle the complexity of the current urban challenges. Culture is understood here in the broadest sense of the word, including the values and conceptualizations of the world, and the modes of technological creation and control of the environment. This concept of culture varies among stakeholders, depending, in particular, on their activities, on the place they live in, and also depending on their scientific background. We propose to complement existing targets that are explicitly related to culture in the UN and UNESCO agendas for 2030, and introduce a target of culture awareness for city information infrastructures. We show that, in the specific case of land patterns and land use, these new targets can be approached with historical data. Our analysis of the related core functionalities is based on interviews with practitioners, draws on insights from the humanities, and takes into account the readiness of the existing technologies.

Along with a new volume of images containing valuable information about our past, the digitization of historical territorial imagery has brought the challenge of understanding and interconnecting collections with unique or rare representation characteristics, and sparse metadata. Content-based image retrieval offers a promising solution in this context, by building links in the data without relying on human supervision. However, while the latest propositions in deep learning have shown impressive results in applications linked to feature learning, they often rely on the hypothesis that there exists a training dataset matching the use case. Increasing generalization and robustness to variations remains an open challenge, poorly understood in the context of real-world applications. Introducing the alegoria benchmark, containing multi-date vertical and oblique aerial digitized photography mixed with more modern street-level pictures, we formulate the problem of low-data, heterogeneous image retrieval, and propose associated evaluation setups and measures. We propose a review of ideas and methods to tackle this problem, extensively compare state-of-the-art descriptors and propose a new multi-descriptor diffusion method to exploit their comparative strengths. Our experiments highlight the benefits of combining descriptors and the compromise between absolute and cross-domain performance.

This article presents a global framework dedicated to the structuring of iconographic heritage collections. To alleviate the poor interlinking both between collections and contents, a first step of automatic linking exploiting content-based image retrieval approaches is evaluated and adapted to the visual variability of such heritage contents. To ensure understanding and analysis of the contents in a structured fashion, a 3D immersive web platform is also introduced alongside visual-based analysis tools. Finally, by exploiting both automatic linking and manual interventions in the visualization platform, an iterative, semi-automatic structuring pipeline is proposed to solve difficult cases missed by automatic structuring, and then improve structuring optimally. Here, we demonstrate the potential of the proposal on the geographic iconographic heritage of Paris, with a dataset of 10k images belonging to several institutions, thus poorly connected nor organized globally.