Assessing Local Low-level Features with Segmentation

Classification of images may help to facilitate object recognition in robotic vision. Computers process pixel information in images for image representation.There are various forms of pixel information which are called low-level features that can be extracted from images. Different feature schemes may suit well in different types of images. Hence, identifying features for image representation is an important step. This paper presents a work in accessing local low-level features to facilitate image classification particularly in natural wildlife images. The images first go through segmentation and every segment is then tiled into image blocks. Different types of image features are extracted from the image segments and also image blocks. The assessment on the local low-level features in image classification is conducted to identify the best image features for image representation in some sample image sets. It was found that the best classification comes from the image blocks represented by the concatenation of LUV colour and Haralick texture features in our dataset.