Derrall Heath

Conveying Semantics through Visual Metaphor

In the field of visual art, metaphor is a way to communicate meaning to the viewer. We present a computational system for communicating visual metaphor that can identify adjectives for describing an image based on a low-level visual feature representation of the image. We show that the system can use this visual-linguistic association to render source images that convey the meaning of adjectives in a way consistent with human understanding. Our conclusions are based on a detailed analysis of how the systems artifacts cluster, how these clusters correspond to the semantic relationships of adjectives as documented in WordNet, and how these clusters correspond to human opinion.

An artistic dialogue with the artificial

In conjunction with Brigham Young Universitys Visual Arts program, we conducted a study centered around a system designed to be an artificial artist, in order to synthesize the ideas of visual artists and computer scientists. Participants from both disciplines designed activities that imposed the limitations of the artificial system on their fellow participants. These activities sparked discussion and insight into the nature of the creative process and how it can be better emulated in artificial systems. We present our system and several of the activities designed around it and discuss the synergistic results.

Semantic Models as a Combination of Free Association Norms and Corpus-Based Correlations

We present computational models capable of understanding and conveying concepts based on word associations. We discover word associations automatically using corpus-based semantic models with Wikipedia as the corpus. The best model effectively combines corpus-based models with preexisting databases of free association norms gathered from human volunteers. We use this model to play human-directed and computer-directed word guessing games (games with a purpose similar to Catch Phrase or Taboo) and show that this model can measurably convey and understand some aspect of word meaning. The results highlight the fact that human-derived word associations and corpus-derived word associations can play complementary roles in semantic models.

IMPROVING MULTILABEL CLASSIFICATION BY AVOIDING IMPLICIT NEGATIVITY WITH INCOMPLETE DATA

Many real‐world problems require multilabel classification, in which each training instance is associated with a set of labels. There are many existing learning algorithms for multilabel classification; however, these algorithms assume implicit negativity, where missing labels in the training data are automatically assumed to be negative. Additionally, many of the existing algorithms do not handle incremental learning in which new labels could be encountered later in the learning process. A novel multilabel adaptation of the backpropagation algorithm is proposed that does not assume implicit negativity. In addition, this algorithm can, using a naïve Bayesian approach, infer missing labels in the training data. This algorithm can also be trained incrementally as it dynamically considers new labels. This solution is compared with existing multilabel algorithms using data sets from multiple domains, and the performance is measured with standard multilabel evaluation metrics. It is shown that our algorithm improves classification performance for all metrics by an overall average of 7.4% when at least 40% of the labels are missing from the training data and improves by 18.4% when at least 90% of the labels are missing.

Fitness function: turning the loop inside out

The process of creating art is an optimization problem for which the objective function is probably unknown and possibly undefinable. That objective function is imposed on the artist by an environment which may be composed of any of a number of sources: peers, a jury, society, the self. This does not imply that the function is arbitrary nor that the optimization is impossible; however, it does suggest an interesting interpretation of the artist at work.