Research on Image Retrieval Optimization Based on Eye Movement Experiment Data

Satisfying a user's actual underlying needs in the image retrieval process is a difficult challenge facing image retrieval technology. The aim of this study is to improve the performance of a retrieval system and provide users with optimized search results using the feedback of eye movement. We analyzed the eye movement signals of the user’s image retrieval process from cognitive and mathematical perspectives. Data collected for 25 designers in eye tracking experiments were used to train and evaluate the model. In statistical analysis, eight eye movement features were statistically significantly different between selected and unselected groups of images (p < 0.05). An optimal selection of input features resulted in overall accuracy of the support vector machine prediction model of 87.16%. Judging the user’s requirements in the image retrieval process through eye movement behaviors was shown to be effective

The Selection of Cabinet Ministers in the Australian Federal Parliament

The two fundamental questions addressed in this thesis are 1) what are the characteristics that are associated with an Australian federal parliamentarian becoming a cabinet minister, and 2) how do these characteristics help a parliamentarian become a cabinet minister? I examine the standard representational and institutional explanations for cabinet appointment decisions such as geography, party/faction, gender and house (Senate vs House of Representatives) and find they do not account for more than 25% of cabinet appointments. I therefore turn to individual characteristics of cabinet ministers. I use education, linguistic/cognitive style, and biographical data to develop a classification model. Using data mining, I isolate three characteristics that explain a high proportion of the appointments to cabinet over the period under examination. These variables are: i) having a legal qualification: ii) entering parliament at an early age: and iii) using abstract language. These three variables explain approximately 78% of cabinet appointments over the period under investigation. I argue that these variables are associated with cabinet appointment because they tap into a particular set of cognitive and behavioural characteristics that are beneficial in demonstrating cabinet potential. An important insight from the analysis is that, in selecting parliamentarians to serve in cabinet, personal factors are more important than representational factors