Multi-feature query language for image classification

Pein, Raoul Pascal; Lu, Joan

doi:10.1016/j.procs.2010.04.287

Cited by 3 publications

(1 citation statement)

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“…Shifts in data science practice require broader structural reforms that contribute to a more just society. As historian Elizabeth Fee notes, "we can expect a sexist society to develop a sexist science; equally, we can expect a feminist society to develop a feminist science" [105] . Similarly, we can expect a militarized society of economic inequality to produce a militarized and unequal data science [106,107] .…”

Section: Engaging With the Broader Political Contextmentioning

confidence: 99%

Data Science as Political Action: Grounding Data Science in a Politics of Justice

Green

2021

J. Soc. Comput.

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In response to public scrutiny of data-driven algorithms, the field of data science has adopted ethics training and principles. Although ethics can help data scientists reflect on certain normative aspects of their work, such efforts are ill-equipped to generate a data science that avoids social harms and promotes social justice. In this article, I argue that data science must embrace a political orientation. Data scientists must recognize themselves as political actors engaged in normative constructions of society and evaluate their work according to its downstream impacts on people's lives. I first articulate why data scientists must recognize themselves as political actors. In this section, I respond to three arguments that data scientists commonly invoke when challenged to take political positions regarding their work. In confronting these arguments, I describe why attempting to remain apolitical is itself a political stance-a fundamentally conservative one-and why data science's attempts to promote "social good" dangerously rely on unarticulated and incrementalist political assumptions. I then propose a framework for how data science can evolve toward a deliberative and rigorous politics of social justice. I conceptualize the process of developing a politically engaged data science as a sequence of four stages. Pursuing these new approaches will empower data scientists with new methods for thoughtfully and rigorously contributing to social justice.

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Section: Engaging With the Broader Political Contextmentioning

confidence: 99%

Data Science as Political Action: Grounding Data Science in a Politics of Justice

Green

2021

J. Soc. Comput.

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Methods Employed

Pein

Renz

2013

Advances in Data Mining and Database Management

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This chapter points out certain technologies that are often applied in a CBIR system. A prototypical retrieval system has been developed in order to evaluate the research hypothesis. Following common principles of information hiding, the software is designed to have multiple layers of abstraction. The top layer needs to be user friendly and also has the task to translate human understandable concepts into machine readable commands. As every user has a different level of expertise, the interface complexity should be adapted accordingly.

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Case Studies

Pein

Renz

2013

Advances in Data Mining and Database Management

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This chapter discusses several case studies to evaluate the methods introduced in chapter 23, Methods. Each case study focuses on a specific issue and the advanced cases build up on previous findings. The first ones are dealing with the low-level fv directly and then the scope widens to the interrelationship of multiple fv until their combination within a single query is used as a mapping rule for higher-level semantics (i.e. categories).

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Multi-feature query language for image classification

Cited by 3 publications

References 12 publications

Data Science as Political Action: Grounding Data Science in a Politics of Justice

Data Science as Political Action: Grounding Data Science in a Politics of Justice

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