We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

IEEE Transactions on Pattern Analysis and Machine Intelligence

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HumanEva datasets contain multiple subjects performing a set of predefined actions with a number of repetitions. On the order of 40,000 frames of synchronized motion capture and multi-view video (resulting in over one quarter million image frames in total) were collected at 60 Hz with an additional 37,000 time instants of pure motion capture data. A standard set of error measures is defined for evaluating both 2D and 3D pose estimation and tracking algorithms. We also describe a baseline algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance Resampling and Annealed Particle Filtering. In the context of this baseline algorithm we explore a variety of likelihood functions, prior models of human motion and the effects of algorithm parameters. Our experiments suggest that image observation models and motion priors play important roles in performance, and that in a multi-view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets and the software are made available to the research community. This infrastructure will support the development of new articulated motion and pose estimation algorithms, will provide a baseline for the evaluation and comparison of new methods, and will help establish the current state of the art in human pose estimation and tracking.

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HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion

In this paper the main problems and the available solutions are addressed for the generation of 3D models from terrestrial images. Close range photogrammetry has dealt for many years with manual or automatic image measurements for precise 3D modelling. Nowadays 3D scanners are also becoming a standard source for input data in many application areas, but image-based modelling still remains the most complete, economical, portable, flexible and widely used approach. In this paper the full pipeline is presented for 3D modelling from terrestrial image data, considering the different approaches and analysing all the steps involved.

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Image-based 3d modelling: a review

In this paper two acquisition methods for archaeological finds are proposed that could help the archaeologist in his work. First we present these very different acquisition methods, stereo and structured light acquisition to get the 3D-surface representation (a so-called 3D-object model) of a sherd. Further we discuss the accuracy of the acquisition methods for archaeological applications. The results are compared with each other and an outlook for a possible fusion of these two methods for an archaeological application is given.

Stereo and Structured Light as Acquisition Methods in the Field of Archaeology

Motivated by the requirements of the present archaeology, we are developing an automated system for archaeological classification and reconstruction of ceramics. This paper shows different acquisition techniques in order to get 3D data of pottery and to compute the profile sections of fragments. With the enhancements shown in this paper, archaeologists get a tool to do archaeological documentation of pottery in a computer assisted way.

3D data retrieval of archaeological pottery

Domes are architectural structural elements characteristic for ecclesiastical and secular monumental buildings, like churches, basilicas, mosques, capitols and city halls In the scope of building facade architectural style classification the current paper addresses the problem of architectural style classification of facade domes Building facade classification by architectural styles is achieved by classification and voting of separate architectural elements, like domes, windows, towers, etc Typical forms of the structural elements bear the signature of each architectural style Our approach classifies domes of three architectural styles - Renaissance, Russian and Islamic We present a three-step approach, which in the first step analyzes the height and width of the dome for the identification of Islamic saucer domes, in the second step detects golden color in YCbCr color space to determine Russian golden onion domes and in the third step performs classification based on dome shapes, using clustering and learning of local features Thus we combine three features - the relation of dome width and height, color and shape, in a single methodology to achieve high classification rate

Architectural Style Classification of Domes

We present basics of a deep learning concept and an overview of well-known deep learning concepts as general Convolutional Neural Networks, R-CNN family, Single Shot Multibox Detector, You Only Look Once architecture and the RetinaNet in the first part of this paper. The all mentioned architectures are described to quickly compare to each other regarding their suitability for given general task. Several selected datasets often used in deep learning competitions are listed in the subsequent chapters in more details. The most known of practically used and listed datasets are COCO, KITTI, PascalVOC and CityShapes. The overview serves as a comparison of the state-of-the-art deep learning methods.

Deep learning concepts and datasets for image recognition: overview 2019

In multispectral imaging multiple discrete wavelength bands are used to image a scene. The imaging process maps the scene contents to different intensity levels and varies the scene appearance from band to band. This induces intensity variations among the spectral images and effects the performance of SIFT for cross spectral image matching. This paper proposes modifications to the SIFT descriptor in order to improve its robustness against spectral variations. The proposed modifications are based on fact, that edges remain well preserved in multispectral imaging and we can achieve better image matching results by boosting the contribution of local edges in the SIFT descriptor construction process. Therefore, we propose a Local Contrast (Δ) and a Differential Excitation (ξ) function for the construction of SIFT descriptors. The experimental results show, that the performance of Δ-SIFT and ξ-SIFT is superior to standard SIFT for image matching under spectral variations.

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Robert Sablatnig

Papers

Stereo and Structured Light as Acquisition Methods in the Field of Archaeology

3D data retrieval of archaeological pottery

Architectural Style Classification of Domes

Deep learning concepts and datasets for image recognition: overview 2019

A Modified SIFT Descriptor for Image Matching under Spectral Variations