3-D Content-Based Retrieval and Classification with Applications to Museum Data

There is an increasing number of multimedia collections arising in areas once only the domain of text and 2-D images. Richer types of multimedia such as audio, video and 3-D objects are becoming more and more common place. However, current retrieval techniques in these areas are not as sophisticated as textual and 2-D image techniques and in many cases rely upon textual searching through associated keywords. This thesis is concerned with the retrieval of 3-D objects and with the application of these techniques to the problem of 3-D object annotation. The majority of the work in this thesis has been driven by the European project, SCULPTEUR. This thesis provides an in-depth analysis of a range of 3-D shape descriptors for their suitability for general purpose and specific retrieval tasks using a publicly available data set, the Princeton Shape Benchmark, and using real world museum objects evaluated using a variety of performance metrics. This thesis also investigates the use of 3-D shape descriptors as inputs to popular classification algorithms and a novel classifier agent for use with the SCULPTEUR system is designed and developed and its performance analysed. Several techniques are investigated to improve individual classifier performance. One set of techniques combines several classifiers whereas the other set of techniques aim to find the optimal training parameters for a classifier. The final chapter of this thesis explores a possible application of these techniques to the problem of 3-D object annotation

An oil painters recognition method based on cluster multiple kernel learning algorithm

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly