Fractal measures of image local features: an application to texture recognition

• URL: http://arxiv.org/abs/2108.12491v1
• Date: Fri, 27 Aug 2021 20:27:28 GMT
• Title: Fractal measures of image local features: an application to texture recognition
• Authors: Pedro M. Silva, Joao B. Florindo
• Abstract summary: We compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector. The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature.
• Score: 1.2183405753834562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Here we propose a new method for the classification of texture images combining fractal measures (fractal dimension, multifractal spectrum and lacunarity) with local binary patterns. More specifically we compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector. The proposal is assessed in the classification of three benchmark databases: KTHTIPS-2b, UMD and UIUC as well as in a real-world problem, namely the identification of Brazilian plant species (database 1200Tex) using scanned images of their leaves. The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature. Such results confirmed the potential of combining a powerful local coding description with the multiscale information captured by the fractal dimension for texture classification.

Related papers

• Deep Neural Networks Fused with Textures for Image Classification [20.58839604333332]
  Fine-grained image classification is a challenging task in computer vision. We propose a fusion approach to address FGIC by combining global texture with local patch-based information. Our method has attained better classification accuracy over existing methods with notable margins.
  arXiv  Detail & Related papers  (2023-08-03T15:21:08Z)
• Multi-View Vertebra Localization and Identification from CT Images [57.56509107412658]
  We propose a multi-view vertebra localization and identification from CT images. We convert the 3D problem into a 2D localization and identification task on different views. Our method can learn the multi-view global information naturally.
  arXiv  Detail & Related papers  (2023-07-24T14:43:07Z)
• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• Multiscale Analysis for Improving Texture Classification [62.226224120400026]
  This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately. We aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector.
  arXiv  Detail & Related papers  (2022-04-21T01:32:22Z)
• Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering [49.75353434786065]
  Detection of inconsistencies of double JPEG artefacts across different image regions is often used to detect local image manipulations. We propose an end-to-end system that can also distinguish regions coming from different donor images.
  arXiv  Detail & Related papers  (2021-02-02T11:21:49Z)
• Reorganizing local image features with chaotic maps: an application to texture recognition [0.0]
  We propose a chaos-based local descriptor for texture recognition. We map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface.
  arXiv  Detail & Related papers  (2020-07-15T03:15:01Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information [14.620086904601472]
  The performance of the proposed method is evaluated in terms of precision and recall on the Simplicity database. The comparative results showed that the proposed approach offers higher precision rate than many known methods.
  arXiv  Detail & Related papers  (2019-12-30T16:11:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.