Ayawo Desire Dandji and Nadia Baaziz, University of Quebec in Outaouais (UQO), Canada

The rapid growth of visual databases calls for efficient Content-Based Image Retrieval (CBIR). Texture descriptors are central to these systems; however, their performance often degrades under geometric image transformations, particularly rotation. This paper presents a CBIR framework designed to compare handcrafted and deep texture features for rotation-invariant retrieval. A novel hybrid approach combines Local Binary Patterns (LBP) with the Stationary Wavelet Transform (SWT) to extract compact, multi-scale descriptors robust to orientation variability. In parallel, a transfer learning strategy leverages intermediate layers of pre-trained convolutional neural networks (VGG16 and ResNet50) with multi-angle feature aggregation to extract rotation-robust deep descriptors. Experiments on benchmark texture datasets (Outex and Kylberg) show that the deep transfer-learning approach achieves higher recall at the cost of larger descriptor dimensionality and greater computational and memory demands, whereas the proposed hybrid descriptor provides a favorable trade-off between accuracy, compactness, and computational efficiency, making it well-suited for resource-constrained applications

CBIR, Handcrafted Texture Feature, Rotation Invariance, Transfer Learning.