PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation

The task of a visual landmark recognition system is to identify photographed buildings or objects in query photos and to provide the user with relevant information on them. With their increasing coverage of the world's landmark buildings and objects, Internet photo collections are now being used as a source for building such systems in a fully automatic fashion. This process typically consists of three steps: clustering large amounts of images by the objects they depict; determining object names from user-provided tags; and building a robust, compact, and efficient recognition index. To this date, however, there is little empirical information on how well current approaches for those steps perform in a large-scale open-set mining and recognition task. Furthermore, there is little empirical information on how recognition performance varies for different types of landmark objects and where there is still potential for improvement. With this paper, we intend to fill these gaps. Using a dataset of 500k images from Paris, we analyze each component of the landmark recognition pipeline in order to answer the following questions: How many and what kinds of objects can be discovered automatically? How can we best use the resulting image clusters to recognize the object in a query? How can the object be efficiently represented in memory for recognition? How reliably can semantic information be extracted? And finally: What are the limiting factors in the resulting pipeline from query to semantics? We evaluate how different choices of methods and parameters for the individual pipeline steps affect overall system performance and examine their effects for different query categories such as buildings, paintings or sculptures

CIM Case Study – June 2007. Strategic Marketing in Practice. The Furze: Analysis

Marketing case study aimed at senior managers aspiring to achieve Chartered Marketer statu

Julian Bream (1933–2020)

Nota do editor: carta para o dossiê da Vórtex em homenagem a Julian Bream

DICTIONARIES AND MANIFOLDS FOR FACE RECOGNITION ACROSS ILLUMINATION, AGING AND QUANTIZATION

During the past many decades, many face recognition algorithms have been proposed. The face recognition problem under controlled environment has been well studied and almost solved. However, in unconstrained environments, the performance of face recognition methods could still be significantly affected by factors such as illumination, pose, resolution, occlusion, aging, etc. In this thesis, we look into the problem of face recognition across these variations and quantization. We present a face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose with dictionaries learned for each class. A novel test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. An image relighting technique based on pose-robust albedo estimation is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms. The problem of recognizing facial images across aging remains an open problem. We look into this problem by studying the growth in the facial shapes. Building on recent advances in landmark extraction, and statistical techniques for landmark-based shape analysis, we show that using well-defined shape spaces and its associated geometry, one can obtain significant performance improvements in face verification. Toward this end, we propose to model the facial shapes as points on a Grassmann manifold. The face verification problem is then formulated as a classification problem on this manifold. We then propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry and integrate it with the Grassmann manifold and the SVM classifier. Experiments show that the proposed method is able to mitigate the variations caused by the aging progress and thus effectively improve the performance of open-set face verification across aging. In applications such as document understanding, only binary face images may be available as inputs to a face recognition algorithm. We investigate the effects of quantization on several classical face recognition algorithms. We study the performances of PCA and multiple exemplar discriminant analysis (MEDA) algorithms with quantized images and with binary images modified by distance and Box-Cox transforms. We propose a dictionary-based method for reconstructing the grey scale facial images from the quantized facial images. Two dictionaries with low mutual coherence are learned for the grey scale and quantized training images respectively using a modified KSVD method. A linear transform function between the sparse vectors of quantized images and the sparse vectors of grey scale images is estimated using the training data. In the testing stage, a grey scale image is reconstructed from the quantized image using the transform matrix and normalized dictionaries. The identities of the reconstructed grey scale images are then determined using the dictionary-based face recognition (DFR) algorithm. Experimental results show that the reconstructed images are similar to the original grey-scale images and the performance of face recognition on the quantized images is comparable to the performance on grey scale images. The online social network and social media is growing rapidly. It is interesting to study the impact of social network on computer vision algorithms. We address the problem of automated face recognition on a social network using a loopy belief propagation framework. The proposed approach propagates the identities of faces in photos across social graphs. We characterize its performance in terms of structural properties of the given social network. We propose a distance metric defined using face recognition results for detecting hidden connections. The performance of the proposed method is analyzed on graph structure networks, scalability, different degrees of nodes, labeling errors correction and hidden connections discovery. The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods. The result also shows it is possible to discover hidden connections in a social network based on face recognition

Facial expression recognition in the wild : from individual to group

The progress in computing technology has increased the demand for smart systems capable of understanding human affect and emotional manifestations. One of the crucial factors in designing systems equipped with such intelligence is to have accurate automatic Facial Expression Recognition (FER) methods. In computer vision, automatic facial expression analysis is an active field of research for over two decades now. However, there are still a lot of questions unanswered. The research presented in this thesis attempts to address some of the key issues of FER in challenging conditions mentioned as follows: 1) creating a facial expressions database representing real-world conditions; 2) devising Head Pose Normalisation (HPN) methods which are independent of facial parts location; 3) creating automatic methods for the analysis of mood of group of people. The central hypothesis of the thesis is that extracting close to real-world data from movies and performing facial expression analysis on movies is a stepping stone in the direction of moving the analysis of faces towards real-world, unconstrained condition. A temporal facial expressions database, Acted Facial Expressions in the Wild (AFEW) is proposed. The database is constructed and labelled using a semi-automatic process based on closed caption subtitle based keyword search. Currently, AFEW is the largest facial expressions database representing challenging conditions available to the research community. For providing a common platform to researchers in order to evaluate and extend their state-of-the-art FER methods, the first Emotion Recognition in the Wild (EmotiW) challenge based on AFEW is proposed. An image-only based facial expressions database Static Facial Expressions In The Wild (SFEW) extracted from AFEW is proposed. Furthermore, the thesis focuses on HPN for real-world images. Earlier methods were based on fiducial points. However, as fiducial points detection is an open problem for real-world images, HPN can be error-prone. A HPN method based on response maps generated from part-detectors is proposed. The proposed shape-constrained method does not require fiducial points and head pose information, which makes it suitable for real-world images. Data from movies and the internet, representing real-world conditions poses another major challenge of the presence of multiple subjects to the research community. This defines another focus of this thesis where a novel approach for modeling the perception of mood of a group of people in an image is presented. A new database is constructed from Flickr based on keywords related to social events. Three models are proposed: averaging based Group Expression Model (GEM), Weighted Group Expression Model (GEM_w) and Augmented Group Expression Model (GEM_LDA). GEM_w is based on social contextual attributes, which are used as weights on each person's contribution towards the overall group's mood. Further, GEM_LDA is based on topic model and feature augmentation. The proposed framework is applied to applications of group candid shot selection and event summarisation. The application of Structural SIMilarity (SSIM) index metric is explored for finding similar facial expressions. The proposed framework is applied to the problem of creating image albums based on facial expressions, finding corresponding expressions for training facial performance transfer algorithms