Tamura features are based on human visual perception and have huge potential in image representation. Conventional Tamura features only work on homogeneous texture images and perform poor on generic images. Therefore, many researchers attempt to improve Tamura features and most of the improvements are based on histogram based representation. Kernel descriptors have been shown to outperform existing histogram based local features as such descriptors do not require coarse quantization of pixel attributes. Instead, in kernel descriptor framework, each pixel equally participates in matching between two image patches. In this paper, we propose a set of kernel descriptors that are based on Tamura features. Additionally, the proposed descriptors are invariant to local rotations. Experimental results show that our proposed approach outperforms the conventional Tamura features significantly.

Wheat is one of the major staple crops across the globe. Therefore, it is mandatory to measure, maintain, and improve the wheat quality for human consumption. Traditional wheat quality measurement methods are mostly invasive, destructive, and limited to small samples of wheat. In a typical supply chain of wheat, there are many receival points where bulk wheat arrives, gets stored, and then gets forwarded as per the requirements. In these receival points, the application of traditional quality measurement methods is difficult and often very expensive. Therefore, there is a need for non-invasive, non-destructive real-time methods for wheat quality assessments. One such method that fulfils the abovementioned criteria is hyperspectral imaging (HSI) for food quality measurement and it can also be applied to bulk samples. In this article, we have investigated how HSI has been used in literature for assessing stored wheat quality. So that the required information to implement real-time quality assessment methods at the different stages of the Australian supply chain can be made available in a single and compact document.

Image representation using feature descriptors is crucial. A number of histogram-based descriptors are widely used for this purpose. However, histogram-based descriptors have certain limitations and kernel descriptors (KDES) are proven to overcome them. Moreover, the combination of more than one KDES performs better than an individual KDES. Conventionally, KDES fusion is performed by concatenating them after the gradient, colour and shape descriptors have been extracted. This approach has limitations in regard to the efficiency as well as the effectiveness. In this paper, we propose a novel approach to fuse different image features before the descriptor extraction, resulting in a compact descriptor which is efficient and effective. In addition, we have investigated the effect on the proposed descriptor when texture-based features are fused along with the conventionally used features. Our proposed descriptor is examined on two publicly available image databases and shown to provide outstanding performances.