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A dataset for Hand-Held Object Recognition
- Source :
- ICIP
- Publication Year :
- 2014
- Publisher :
- IEEE, 2014.
-
Abstract
- Visual object recognition is just one of the many applications of camera-equipped smartphones. The ability to recognise objects through photos taken with wearable and handheld cameras is already possible through some of the larger internet search providers; yet, there is little rigorous analysis of the quality of search results, particularly where there is great disparity in image quality. This has motivated us to develop the Small Hand-held Object Recognition Test (SHORT). This includes a dataset that is suitable for recognising hand-held objects from either snapshots or videos acquired using hand-held or wearable cameras. SHORT provides a collection of images and ground truth that help evaluate the different factors that affect recognition performance. At its present state, the dataset is comprised of a set of high quality training images and a large set of nearly 135,000 smartphone-captured test images of 30 grocery products. In this paper, we will discuss some open challenges in the visual object recognition of objects that are being held by users. We evaluate the performance of a number of popular object recognition algorithms, with differing levels of complexity, when tested against SHORT.
- Subjects :
- Image quality
business.industry
Computer science
Intelligent character recognition
3D single-object recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Cognitive neuroscience of visual object recognition
Wearable computer
Content-based image retrieval
Computer vision
Artificial intelligence
Set (psychology)
business
Mobile device
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2014 IEEE International Conference on Image Processing (ICIP)
- Accession number :
- edsair.doi...........003841474a75270670fde3c3b8a42e65