DeepLumina: A Method Based on Deep Features and Luminance Information for Color Texture Classification

Abstract

Color texture classification is a significant computer vision task to identify and categorize textures that we often observe in natural visual scenes in the real world. Without color and texture, it remains a tedious task to identify and recognize objects in nature. Deep architectures proved to be a better method for recognizing the challenging patterns from texture images. This paper proposes a method, DeepLumina, that uses features from the deep architectures and luminance information with RGB color space for efficient color texture classification. This technique captures convolutional neural network features from the ResNet101 pretrained models and uses luminance information from the luminance (Y) channel of the YIQ color model and performs classification with a support vector machine (SVM). This approach works in the RGB-luminance color domain, exploring the effectiveness of applying luminance information along with the RGB color space. Experimental investigation and analysis during the study show that the proposed method, DeepLumina, got an accuracy of 90.15% for the Flickr Material Dataset (FMD) and 73.63% for the Describable Textures dataset (DTD), which is highly promising. Comparative analysis with other color spaces and pretrained CNN-FC models are also conducted, which throws light into the significance of the work. The method also proved the computational simplicity and obtained results in lesser computation time.

WaveTexNeT: Ensemble Based Wavelet-Xception Deep Neural Network Architecture for Color Texture Classification

“…Liu et al [24] used CWTACapsNet, with wavelet and compressed tensor self attention model to get 81.52%. Simon and Uma [46] utilizes deep features and luminance information along with the machine learning classifier and produced an accuracy of 73.63%. WaveTexNeT obtained a good accuracy of 89.01% for luminance from YCbCr, 90.34% for luminance from YIQ color space.…”

“…used the Scale invariant feature transform (SIFT) and IFV for getting 69.60% accuracy. Deep Lumina[46] obtained an accuracy of 90.15%. Mao et al[44] presented a method Deep residual pooling network obtained an accuracy of 85.72%.…”

Weather Forecast Based on Color Cloud Image Recognition under the Combination of Local Image Descriptor and Histogram Selection

“…Previous studies focus on color features/color information of the RGB color space to classify clouds/sky images. However, the specific color space allows an improvement in the classification performance [32,33]. This work considers 14 different color spaces, such as HLS, HSV, IHLS, Lab, rgb, YIQ, YUV, RGB, bwrgby, XYZ, YCbCr, Luv, I1I2I3, ISH, for extracting features.…”

Attention-Guided Deep Learning Texture Feature for Object Recognition Applications

“…Jia et al [18] used a two-stage recurrent neural network to extract shape and texture features, while Kasthuri et al [19] combined deep learning with Gabor filters for face recognition. Simon et al [20] combined deep architecture features with luminance information. Bello et al [21] utilized CNN to extract color texture features, revealing superior discriminative ability compared to hand-crafted descriptors.…”