9 research outputs found
Learning Colour Representations of Search Queries
Image search engines rely on appropriately designed ranking features that
capture various aspects of the content semantics as well as the historic
popularity. In this work, we consider the role of colour in this relevance
matching process. Our work is motivated by the observation that a significant
fraction of user queries have an inherent colour associated with them. While
some queries contain explicit colour mentions (such as 'black car' and 'yellow
daisies'), other queries have implicit notions of colour (such as 'sky' and
'grass'). Furthermore, grounding queries in colour is not a mapping to a single
colour, but a distribution in colour space. For instance, a search for 'trees'
tends to have a bimodal distribution around the colours green and brown. We
leverage historical clickthrough data to produce a colour representation for
search queries and propose a recurrent neural network architecture to encode
unseen queries into colour space. We also show how this embedding can be learnt
alongside a cross-modal relevance ranker from impression logs where a subset of
the result images were clicked. We demonstrate that the use of a query-image
colour distance feature leads to an improvement in the ranker performance as
measured by users' preferences of clicked versus skipped images.Comment: Accepted as a full paper at SIGIR 202
Estimating Color-Concept Associations from Image Statistics
To interpret the meanings of colors in visualizations of categorical
information, people must determine how distinct colors correspond to different
concepts. This process is easier when assignments between colors and concepts
in visualizations match people's expectations, making color palettes
semantically interpretable. Efforts have been underway to optimize color
palette design for semantic interpretablity, but this requires having good
estimates of human color-concept associations. Obtaining these data from humans
is costly, which motivates the need for automated methods. We developed and
evaluated a new method for automatically estimating color-concept associations
in a way that strongly correlates with human ratings. Building on prior studies
using Google Images, our approach operates directly on Google Image search
results without the need for humans in the loop. Specifically, we evaluated
several methods for extracting raw pixel content of the images in order to best
estimate color-concept associations obtained from human ratings. The most
effective method extracted colors using a combination of cylindrical sectors
and color categories in color space. We demonstrate that our approach can
accurately estimate average human color-concept associations for different
fruits using only a small set of images. The approach also generalizes
moderately well to more complicated recycling-related concepts of objects that
can appear in any color.Comment: IEEE VIS InfoVis 2019 ACM 2012 CSS: 1) Human-centered computing,
Human computer interaction (HCI), Empirical studies in HCI 2) Human-centered
computing, Human computer interaction (HCI), HCI design and evaluation
methods, Laboratory experiments 3) Human-centered computing, Visualization,
Empirical studies in visualizatio
C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model
Interior color design is a creative process that endeavors to allocate colors
to furniture and other elements within an interior space. While much research
focuses on generating realistic interior designs, these automated approaches
often misalign with user intention and disregard design rationales. Informed by
a need-finding preliminary study, we develop C2Ideas, an innovative system for
designers to creatively ideate color schemes enabled by an intent-aligned and
domain-oriented large language model. C2Ideas integrates a three-stage process:
Idea Prompting stage distills user intentions into color linguistic prompts;
Word-Color Association stage transforms the prompts into semantically and
stylistically coherent color schemes; and Interior Coloring stage assigns
colors to interior elements complying with design principles. We also develop
an interactive interface that enables flexible user refinement and
interpretable reasoning. C2Ideas has undergone a series of indoor cases and
user studies, demonstrating its effectiveness and high recognition of
interactive functionality by designers.Comment: 26 pages, 11 figure
A Systematic and Minimalist Approach to Lower Barriers in Visual Data Exploration
With the increasing availability and impact of data in our lives, we need to make quicker, more accurate, and intricate data-driven decisions. We can see and interact with data, and identify relevant features, trends, and outliers through visual data representations. In addition, the outcomes of data analysis reflect our cognitive processes, which are strongly influenced by the design of tools. To support visual and interactive data exploration, this thesis presents a systematic and minimalist approach.
First, I present the Cognitive Exploration Framework, which identifies six distinct cognitive stages and provides a high-level structure to design guidelines, and evaluation of analysis tools. Next, in order to reduce decision-making complexities in creating effective interactive data visualizations, I present a minimal, yet expressive, model for tabular data using aggregated data summaries and linked selections. I demonstrate its application to common categorical, numerical, temporal, spatial, and set data types. Based on this model, I developed Keshif as an out-of-the-box, web-based tool to bootstrap the data exploration process. Then, I applied it to 160+ datasets across many domains, aiming to serve journalists, researchers, policy makers, businesses, and those tracking personal data.
Using tools with novel designs and capabilities requires learning and help-seeking for both novices and experts. To provide self-service help for visual data interfaces, I present a data-driven contextual in-situ help system, HelpIn, which contrasts with separated and static videos and manuals. Lastly, I present an evaluation on design and graphical perception for dense visualization of sorted numeric data. I contrast the non-hierarchical treemaps against two multi-column chart designs, wrapped bars and piled bars. The results support that multi-column charts are perceptually more accurate than treemaps, and the unconventional piled bars may require more training to read effectively.
This thesis contributes to our understanding on how to create effective data interfaces by systematically focusing on human-facing challenges through minimalist solutions. Future work to extend the power of data analysis to a broader public should continue to evaluate and improve design approaches to address many remaining cognitive, social, educational, and technical challenges
Modelling fashion microblogs to increase the influence of social media marketing in Ireland and China
With the breakthrough of social media in the 21st century, microblogging has become an influential medium for marketing fashion brands and products online. For this reason, this study explores ten Irish and another ten Chinese fashion microblogging influencers’ microblogs using Text Mining and Netnography. By this comparison, the study finds a current model of how fashion microblogs influence fashion consumption in Ireland and China. With the help of this model, the study proposes a typology of Irish and Chinese fashion microblogging influencers and their basic microblogging strategies. The proposed typology intends to help fashion marketers to model their fashion microblogs in order to increase the influence of social media marketing in Ireland and China. Furthermore, the proposed typology is applied to develop a digital artefact that not only can deal with Irish and Chinese fashion microblogs at the same time but also show the results employing text visualisation. This bilingual digital website tries to make up for the lack of attention to text analysis on fashion-related words in the development of text mining tools. Finally, the methodological combination of Text Mining and Netnography employs digital tools and computer programming to conduct studies in the field of arts and humanities. The success of methodological combination in the study opens up a bright prospect for interdisciplinary research methodology