Image classification is one of the main research problems in computer vision and machine learning. Since in most real-world image classification applications there is no control over how the images are captured, it is necessary to consider the possibility that these images might be affected by noise (e.g. sensor noise in a low-quality surveillance camera). In this paper we analyse the impact of three different types of noise on descriptors extracted by two widely used feature extraction methods (LBP and HOG) and how denoising the images can help to mitigate this problem. We carry out experiments on two different datasets and consider several types of noise, noise levels, and denoising methods. Our results show that noise can hinder classification performance considerably and make classes harder to separate. Although denoising methods were not able to reach the same performance of the noise-free scenario, they improved classification results for noisy data.
In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures. Color features for recognition are often extracted from 8-bit images. However, some studies recommended the use of an arbitrary number of colors, often less than 256 colors. Because the use of less colors can led to a lower computational cost and less power consumption, this paper investigates the extraction of features using images with different pixel depth, i.e., number of colors, and using two resolution settings. We show that it is possible to obtain compact and effective descriptors, by extracting features with images of lower quantization and resolution parameters. Also, we propose a bitwise quantization algorithm that codifies the most significant color features. While it reduces the number of colors, the distances between the feature vectors are kept similar, benefiting mobile image applications. The image-based visual recognition pipeline includes a step that converts color images into images with a single channel, obtaining a color-quantized image that can be processed by feature extraction methods. In this paper we explore this step in order to produce compact features that can be used in retrieval and classification systems. We show that different quantization methods produce very different results in terms of accuracy. While compared with more complex methods, this procedure allows the feature extraction in order to achieve a significant dimensionality reduction, while preserving or improving system accuracy. The results indicate that quantization simplify images before feature extraction and dimensionality reduction, producing more compact vectors and reducing system complexity.