A Two-Layer Local Constrained Sparse Coding Method for Fine-Grained Visual Categorization

2015-05-11 · Guo Lihua, Guo Chenggan

Fine-grained categories are more difficulty distinguished than generic categories due to the similarity of inter-class and the diversity of intra-class. Therefore, the fine-grained visual categorization (FGVC) is considered as one of challenge problems in computer vision recently. A new feature learning framework, which is based on a two-layer local constrained sparse coding architecture, is proposed in this paper. The two-layer architecture is introduced for learning intermediate-level features, and the local constrained term is applied to guarantee the local smooth of coding coefficients. For extracting more discriminative information, local orientation histograms are the input of sparse coding instead of raw pixels. Moreover, a quick dictionary updating process is derived to further improve the training speed. Two experimental results show that our method achieves 85.29% accuracy on the Oxford 102 flowers dataset and 67.8% accuracy on the CUB-200-2011 bird dataset, and the performance of our framework is highly competitive with existing literatures.

📄 PDF Abstract BibTeX arXiv:1505.02505

Code (0)

등록된 구현이 없습니다.

Tasks

DiversityFine-Grained Visual Categorization

You Only Index Once: Cross-Layer Sparse Attention with Shared Routing

2026-06-04 · Yutao Sun, Yanqi Zhang, Li Dong, Jianyong Wang 외 arxiv

Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate long intermediate chains of thought. Existing sparse attention method…

Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI

2026-08-21 · Shiva Shrestha, Kazi Shaharair Sharif, Zongxing Xie, Jiajing Huang 외 arxiv

Federated fine-tuning enables large language models to adapt on edge devices without centralizing private data, but practical deployments must address hardware instability and adversarial update corruption together. Ther…

Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery

2019-08-21 · Zhao Zhang, Lei Wang, Sheng Li, Yang Wang 외

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, …

Representation Learning

Deep Coding Network

2010-12-01 · NeurIPS 2010 12 · Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu

This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is derived based on theoretical analysis of nonlinear functional approximation that ex…

Unsupervised Feature Learning by Deep Sparse Coding

2013-12-20 · Yunlong He, Koray Kavukcuoglu, Yun Wang, Arthur Szlam 외

In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovati…

Object Recognition