Texture classification using transform analysis

The work presented in this thesis deals with the application of spectral methods for texture classification. The aim of the present work is to introduce a hybrid methodology for texture classification based on a spatial domain global pre-classifier together with a spectral classifier that utilizes multiresolution transform analysis. The reason for developing a spatial pre-classifier is that many discriminating features of textures are present in the spatial domain of the texture. Of these, global features such as intensity histograms and entropies can still add significant information to the texture classification process. The pre-classifier uses texture intensity histograms to derive histogram moments that serve as global features. A spectral classifier that uses Hartley transform follows the pre-classifier. The choice of such transform was due to the fact that the Fast Hartley Transform has many advantages over the other transforms since it results in real valued arrays and requires less memory space and computational complexity. To test the performance of the whole classifier, 900 texture images were generated using mathematical texture generating functions. The images generated were of three different classes and each class is sub-classified into three sub-classes. Half of the generated samples was used to build the classifier, while the other half was used to test it. The pre-classifier was designed to identify texture classes using an Euclidean distance matching for 4 statistical moments of the intensity histograms. The pre-classifier matching accuracy is found to be 99.89%. The spectral classifier is designed on the basis of the Hartley transform to determine the image sub-class. Initially, a full resolution Hartley transform was used to obtain two orthogonal power spectral vectors. Peaks in these two vectors were detected after applying a 10% threshold and the highest 4 peaks for each image are selected and saved in position lookup tables. The matching accuracy obtained using the two classification phases (pre-classifier and spectral classifier) is 99.56%. The accuracy achieved for the single resolution classifier is high but that was achieved on the expense of space for the lookup tables. In order to investigate the effect of lowering the resolution on the size of the information needed for matching the textures, we have applied a multiresolution technique to the Hartley Transform in a restricted way by computing the Hartley spectra in decreasing resolution. In particular, a one-step resolution decrease achieves 99% matching efficiency while saving memory space by 40%. This is a minor sacrifice of less than 1% in the matching efficiency with a considerable decrease in the complexity of the present methodology

A game-based approach towards human augmented image annotation.

PhDImage annotation is a difficult task to achieve in an automated way. In this thesis, a human-augmented approach to tackle this problem is discussed and suitable strategies are derived to solve it. The proposed technique is inspired by human-based computation in what is called “human-augmented” processing to overcome limitations of fully automated technology for closing the semantic gap. The approach aims to exploit what millions of individual gamers are keen to do, i.e. enjoy computer games, while annotating media. In this thesis, the image annotation problem is tackled by a game based framework. This approach combines image processing and a game theoretic model to gather media annotations. Although the proposed model behaves similar to a single player game model, the underlying approach has been designed based on a two-player model which exploits the player’s contribution to the game and previously recorded players to improve annotations accuracy. In addition, the proposed framework is designed to predict the player’s intention through Markovian and Sequential Sampling inferences in order to detect cheating and improve annotation performances. Finally, the proposed techniques are comprehensively evaluated with three different image datasets and selected representative results are reported