Robust image retrieval for lacy and embroidered fabric

Abstract

Image retrieval is a task which retrieves similar images from a large database based on a given input query image. The lacy and embroidered fabric contains repetitive patterns and rich texture, making the image retrieval difficult. The GIST feature is a spatial information feature that performs well on retrieving images with duplicate patterns. Speeded-up robust features (SURF) feature is invariant to rotation, which makes it powerful in retrieving rotated images. The method proposed in this paper is to combine the benefits of both GIST and SURF features, supporting the image retrieval from a fabric image database. In addition, we extract the structure from the texture via the relative total variation to eliminate the influence of complex texture on the feature point extraction. A key insight and contribution of our paper is that the combination enables accurate fabric image retrieval, especially for rotated images. To demonstrate the robustness and accuracy of our method, we applied it to a database that contains 527 fabric images. The experimental results show that the proposed algorithm outperforms the state-of-the-art methods on the fabric images with hollow and embroidery patterns.

Research progress of content-based fabric image retrieval

“…Taking the two striped fabrics shown in Figure 6 as an example, the two images have similar stripe arrangements, but they are not similar in microstructure due to different underlying fabric weaves. The textures composed of fine-grained units such as the underlying fabric weave, density, yarn count, and so 12,13 ACF, 15 local binary pattern (LBP), 21,38 ORB, 39 grey-level co-occurrence matrix, 40 SIFT 41 Frequency Garbor, 13,19,42 Fourier transform, 21 wavelet transform [42][43][44][45] Markov model, 20 fractal model 46…”

“…Kang et al. 12 and Yao and Ke 13 used the relative total variation method to remove the base weave in the fabric image while preserving the pattern structure. The relative total variation method 14 can effectively decompose the structure information and texture in the image, and this can be adopted to remove the base texture.…”

Content-based image retrieval for fabric images: A survey

“…Traditional descriptors for lacy and embroidered fabric image were combined in a research carried out by [14], which combined GIST and SURF for spatial and texture features. The fabric image dataset contained many patterns involving repetition, with an outstanding performance of the GIST feature description for image retrieval with identical patterns.…”

“…Second, it contains complex textures that are more critical than color features for fabric image retrieval [34]. Third, it has repetitive patterns and creamy textures [9], [14]. Fourth, fabric images with indistinct embedded lines decreased the average precision [11].…”

“…A combination of techniques that can handle texture and color features is needed to improve accuracy for image types with complex textures. [7], [8], [10], [11], [13], [15], [19] Small feature dimensions [8], [15], [16], [21] Faster computation time [8], [9], [12], [15] Can handle background interference and scene rotation [14], [53] Not suitable for complex fabric images [7], [9], [11] Use of a single feature has low retrieval accuracy [6], [8], [11], [12], [17], [18], [28]. Retrieval accuracy is lower than with CNNs…”