Image to text translation by multi-label classification

This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. A multi-label learning method is suggested for realizing the translation component. Empirical studies show that the predictive performance of the translation component is better than its counterparts when employed a dual-random ensemble multi-label classification algorithm.

Image to text translation by multi-label classification

This thesis includes the development of an architectural framework for the proposed image to text translation system containing four components. Selection of appropriate algorithms for the first three components developed three effective multi-label classification algorithms for the fourth component, i.e. the translation component, for different problem settings.

Empirical study of multi-label classification methods for image annotation and retrieval

This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and binary relevance (BR) learning algorithms perform well on Corel image dataset. Based on the overall evaluation results, examples are given to show label prediction performance for the algorithms using selected image examples. This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.

A triple-random ensemble classification method for mining multi-label data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.

Continuous product innovation : a comparison of key elements across different contingency sets

This paper discusses results from an international study of continuous improvement in product innovation. The empirical research is based upon a theoretical model of continuous product innovation (CPI) that identifies contingencies, behaviours, levers and performances relevant to improving product innovation processes. As successful knowledge management is widely recognised as a key capability for firms to successfully develop CPI, companies have been classified according to identified contingencies and the impact of these contingencies on key knowledge management criteria. Comparative analysis of the identified groups of companies has demonstrated important differences between the learning behaviours found present in the two groups thus identified, and in the levers used to develop and support these behaviours. The selection of performance measures by the two groups has highlighted further significant differences in the way the two groups understand and measure their CPI processes. Finally, the paper includes a discussion of appropriate mechanisms for firms with similar contingency sets to improve their approaches to organisational learning and product innovation.