The visual preferences for forest regeneration and field afforestation : four case studies in Finland

The overall aim of this dissertation was to study the public's preferences for forest regeneration fellings and field afforestations, as well as to find out the relations of these preferences to landscape management instructions, to ecological healthiness, and to the contemporary theories for predicting landscape preferences. This dissertation includes four case studies in Finland, each based on the visualization of management options and surveys. Guidelines for improving the visual quality of forest regeneration and field afforestation are given based on the case studies. The results show that forest regeneration can be connected to positive images and memories when the regeneration area is small and some time has passed since the felling. Preferences may not depend only on the management alternative itself but also on the viewing distance, viewing point, and the scene in which the management options are implemented. The current Finnish forest landscape management guidelines as well as the ecological healthiness of the studied options are to a large extent compatible with the public's preferences. However, there are some discrepancies. For example, the landscape management instructions as well as ecological hypotheses suggest that the retention trees need to be left in groups, whereas people usually prefer individually located retention trees to those trees in groups. Information and psycho-evolutionary theories provide some possible explanations for people's preferences for forest regeneration and field afforestation, but the results cannot be consistently explained by these theories. The preferences of the different stakeholder groups were very similar. However, the preference ratings of the groups that make their living from forest - forest owners and forest professionals - slightly differed from those of the others. These results provide support for the assumptions that preferences are largely consistent at least within one nation, but that knowledge and a reference group may also influence preferences.Väitöskirjassa tutkittiin ihmisten maisemapreferenssejä (maisemallisia arvostuksia) metsänuudistamishakkuiden ja pellonmetsitysten suhteen sekä analysoitiin näiden preferenssien yhteyksiä maisemanhoito-ohjeisiin, vaihtoehtojen ekologiseen terveyteen ja preferenssejä ennustaviin teorioihin. Väitöskirja sisältää neljä tapaustutkimusta, jotka perustuvat hoitovaihtoehtojen visualisointiin ja kyselytutkimuksiin. Tapaustutkimusten pohjalta annetaan ohjeita siitä, kuinka uudistushakkuiden ja pellonmetsitysten visuaalista laatua voidaan parantaa. Väitöskirjan tulokset osoittavat, että uudistamishakkuut voivat herättää myös myönteisiä mielikuvia ja muistoja, jos uudistusala on pieni ja hakkuun välittömät jäljet ovat jo peittyneet. Preferensseihin vaikuttaa hoitovaihtoehdon lisäksi mm. katseluetäisyys, katselupiste ja ympäristö, jossa vaihtoehto on toteutettu. Eri viiteryhmien (metsäammattilaiset, pääkaupunkiseudun asukkaat, ympäristönsuojelijat, tutkimusalueiden matkailijat, paikalliset asukkaat sekä metsänomistajat) maisemapreferenssit olivat hyvin samankaltaisia. Kuitenkin ne ryhmät, jotka saavat ainakin osan elannostaan metsästä - metsänomistajat ja metsäammattilaiset - pitivät metsänhakkuita esittävistä kuvista hieman enemmän kuin muut ryhmät. Nämä tulokset tukevat oletusta, että maisemapreferenssit ovat laajalti yhteneväisiä ainakin yhden kansan tai kulttuurin keskuudessa, vaikka myös viiteryhmä saattaa vaikuttaa preferensseihin jonkin verran. Nykyiset metsämaisemanhoito-ohjeet ovat pitkälti samankaltaisia tässä väitöskirjassa havaittujen maisemapreferenssien kanssa. Myöskään tutkittujen vaihtoehtoisten hoitotapojen ekologisen paremmuuden ja niihin kohdistuvien maisemallisten arvostusten välillä ei ollut suurta ristiriitaa. Kuitenkin joitakin eroavaisuuksia oli; esimerkiksi sekä maisemanhoito-ohjeiden että ekologisten hypoteesien mukaan säästöpuut tulisi jättää ryhmiin, kun taas ihmiset pitivät eniten yksittäin jätetyistä puista. Informaatiomalli ja psyko-evolutionaarinen teoria tarjoavat mahdollisia selityksiä uudistushakkuisiin ja pellonmetsitykseen kohdistuville preferensseille, vaikkakaan tutkimuksen tuloksia ei voida täysin selittää näillä teorioilla

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Understanding Image Virality

Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -- better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different `contexts' in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.Comment: Pre-print, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

Visual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.Comment: 10 pages, 4 figures, 4 table

VIP: Finding Important People in Images

People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context of the event. This paper analyzes the concept of the importance of individuals in group photographs. We address two specific questions -- Given an image, who are the most important individuals in it? Given multiple images of a person, which image depicts the person in the most important role? We introduce a measure of importance of people in images and investigate the correlation between importance and visual saliency. We find that not only can we automatically predict the importance of people from purely visual cues, incorporating this predicted importance results in significant improvement in applications such as im2text (generating sentences that describe images of groups of people)