Automatic Gemstone Classification Using Computer Vision

This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas.

A Copula Based Method for the Classification of Fish Species

The proposed work develops a method for classification of the species of a fish given in an image, which is a sub-ordinate level classification problem. Fish image categorization is unique and challenging as the images of same fish species can show significant differences in the fish's attributes when taken in different conditions. The authors' approach analyses the local patches of images, cropped based on specific body parts, and hence keep comparison more specific to grab more finer details rather than comparing global postures. The authors have used Histogram of Oriented Gradients and colour histograms to create representative feature vectors; feature vectors are summarized using Copula theory. Their method is very simple yet they have matched the classification accuracy of other proposed complex work for such problems.

Multi-Region Scene Matching Based Localisation for Autonomous Vision Navigation of UAVs

A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.

Compressed-Domain Image Retrieval Based on Colour Visual Patterns

Image retrieval and image compression have been typically pursued separately. Only little research has been done on a synthesis of the two by allowing image retrieval to be performed directly in the compressed domain of images without the need to uncompress them first. In this chapter the authors show that such compressed domain image retrieval can indeed be done and lead to effective and efficient retrieval performance. They introduce a novel compression algorithm – colour visual pattern image coding (CVPIC) – and present several retrieval algorithms that operate directly on compressed CVPIC data. Their experiments demonstrate that it is not only possible to realise such midstream content access, but also that the presented techniques outperform standard retrieval techniques such as colour histograms and colour correlograms.