On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Trade mark similarity assessment support system

Trade marks are valuable intangible intellectual property (IP) assets with potentially high reputational value that can be protected. Similarity between trade marks may potentially lead to infringement. That similarity is normally assessed based on the visual, conceptual and phonetic aspects of the trade marks in question. Hence, this thesis addresses this issue by proposing a trade mark similarity assessment support system that uses the three main aspects of trade mark similarity as a mechanism to avoid future infringement. A conceptual model of the proposed trade mark similarity assessment support system is first proposed and developed based on the similarity assessment criteria outlined in a trade mark manual. The proposed model is the first contribution of this study, and it consists of visual, conceptual, phonetic and inference engine modules. The second contribution of this work is an algorithm that compares trade marks based on their visual similarity. The algorithm performs a similarity assessment using content-based image retrieval (CBIR) technology and an integrated visual descriptor derived using the low-level image feature, i.e. the shape feature. The performance of the algorithm is then assessed using information retrieval based measures. The obtained result demonstrates better retrieval performance in comparison to the state of the art algorithm. The conceptual aspect of trade mark similarity is then examined and analysed using a proposed algorithm that employs semantic technology in the conceptual module. This contribution enables the computation of the conceptual similarity between trade marks, with the utilisation of an external knowledge source in the form of a lexical ontology, together with natural language processing and set similarity theory. The proposed algorithm is evaluated using both information VI retrieval and human collective opinion measures. The retrieval result produced by the proposed algorithm outperforms the traditional string similarity comparison algorithm in both measures. The phonetic module examines the phonetic similarity of trade marks using another proposed algorithm that utilises phoneme analysis. This algorithm employs phonological features, which are extracted based on human speech articulation. In addition, the algorithm also provides a mechanism to compare the phonetic aspect of trade marks with typographic characters. The proposed algorithm is the fourth contribution of this study. It is evaluated using an information retrieval based measure. The result shows better retrieval performance in comparison to the traditional string similarity algorithm. The final contribution of this study is a methodology to aggregate the overall similarity score between trade marks. It is motivated by the understanding that trade mark similarity should be assessed holistically; that is, the visual, conceptual and phonetic aspects should be considered together. The proposed method is developed in the inference engine module; it utilises fuzzy logic for the inference process. A set of fuzzy rules, which consists of several membership functions, is also derived in this study based on the trade mark manual and a collection of trade mark disputed cases is analysed. The method is then evaluated using both information retrieval and human collective opinion. The proposed method improves the retrieval accuracy and the experiment also proves that the aggregated similarity score correlates well with the score produced from human collective opinion. The evaluations performed in the course of this study employ the following datasets: the MPEG-7 shape dataset, the MPEG-7 trade marks dataset, a collection of 1400 trade marks from real trade mark dispute cases, and a collection of 378,943 company names

Moment-based Techniques for Image Retrieval

In this paper we analyze some shape-based image retrieval methods which use different types of geometric and algebraic moments and Fourier descriptors. Moments have been widely used in pattern recognition applications to describe the geometrical characteristics of different objects. They provide fundamental geometric properties (e.g. area, centroid, moment of inertia, etc..). We consider various description techniques: Hu, Flusser and Taubin invariants, Legendre and Zernike moments, Generic Fourier Descriptors (GFD). The set of absolute orthogonal (i.e. rotation) moment invariants defined by Hu can be used for scale, position, and rotation invariant pattern identification. Flusser' s complete set of invariants appears as a particular case, with invariance only to rotation. The Taubin's affine moment invariants introduce the concept of covariant matrix. Legendre moments are based on orthogonal Legendre polynomials and are not invariant under image rotation. Zernike moments consist of a set of complex polynomials that form a complete orthogonal set over the interior of the unit circle. GFDs are derived by applying a modified polar Fourier transform on shape image. We have applied the retrieval methods on a collection of images chosen from MPEG7 database. The image retrieval performance of each method is described by the precision-recall graph. In the paper we propose a novel approach that combines the described techniques after a coarse partitioning of the image dataset by their morphological features