.png){kind=icon}
.png){kind=icon}
Return
Unseen object categorization using multiple visual cues
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2016.12.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose an object categorization framework to extract different visual cues and tackle the problem of categorizing previously unseen objects under various viewpoints. Specifically, we decompose the input image into three visual cues: structure, texture and shape cues. Then, local features are extracted using the log-polar transform to achieve scale and rotation invariance. The local descriptors obtained from different visual cues are fused using the bag-of-words representation with some key contributions: (1) a keypoint detection scheme based on variational calculus is proposed for selecting sampling locations; (2) a codebook optimization scheme based on discrete entropy is proposed to choose the optimal codewords and at the same time increase the overall performance. We tested the proposed object classification framework on the ETH-80 dataset using the leave-one-object-out protocol to specifically tackle the problem of categorizing previously unseen objects under various viewpoints. On this popular dataset, the proposed object categorization system obtained a very high improvement in classification performance compared to state-of-the-art methods.
Keywords:
Log-polar transform
Object classification
Structure-texture decomposition
Shape extraction
Bag-of-words model
ETH-80 dataset
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.