Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram

2015-03-12 · Nagaraja S., Prabhakar C. J.

In this paper, we present a novel approach for image retrieval based on extraction of low level features using techniques such as Directional Binary Code, Haar Wavelet transform and Histogram of Oriented Gradients. The DBC texture descriptor captures the spatial relationship between any pair of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore, DBC captures more spatial information than LBP and its variants, also it can extract more edge information than LBP. Hence, we employ DBC technique in order to extract grey level texture feature from each RGB channels individually and computed texture maps are further combined which represents colour texture features of an image. Then, we decomposed the extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the shape and local features of wavelet transformed images using Histogram of Oriented Gradients for content based image retrieval. The performance of proposed method is compared with existing methods on two databases such as Wang's corel image and Caltech 256. The evaluation results show that our approach outperforms the existing methods for image retrieval.

📄 PDF Abstract BibTeX arXiv:1503.03606

Code (0)

등록된 구현이 없습니다.

Tasks

Content-Based Image Retrieval Image Retrieval Retrieval

Texture image retrieval using DTCWT-SVD and local binary pattern features

2017-12-01 · Journal of Information Processing Systems 2017 12 · Dayou, Jiang; Jongweon, Kim

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low-level texture features were combined in the approach. One of them was extracted from singular va…

Image Retrieval Retrieval Texture Image Retrieval

Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding

2026-05-06 · Jingtao Liu, Peiliang Gong, Chuhang Zheng, Yiheng Liu 외 arxiv

EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval. However, limited paired data and a fundamental mismatch between high-fidelity digital images and biol…

Representation Learning Contrastive Learning Image Retrieval

Intra-Modal Constraint Loss For Image-Text Retrieval

2022-07-11 · Jianan Chen, Lu Zhang, Qiong Wang, Cong Bai 외

Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains. With the development of convolutional and recurrent neural networks, the bottleneck of retrieval across imag…

Cross-Modal Retrieval Image-text Retrieval Retrieval Text Retrieval

Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning

2019-01-04 · journal 2019 1 · Zar Nawab Khan Swati, Qinghua Zhao3, Muhammad Kabir, Farman Ali 외

This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR systems for MR images is the …

Content-Based Image Retrieval Image Retrieval Metric Learning Retrieval+2

Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information

2022-04-21 · Zhiqiang Yuan, Wenkai Zhang, Changyuan Tian, Xuee Rong 외

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, curr…

Cross-Modal Retrieval Image Retrieval Retrieval