Building a Large Scale Image Database
Listen to the summary
Uses a voice available on your device
Audio options
On this page
Key Takeaways
- The project utilizes the WordNet hierarchy to provide a semantic backbone for image categorization.
- Data collection was scaled by leveraging the Amazon Mechanical Turk workforce.
- The resulting dataset currently encompasses 12 subtrees containing 5247 synsets and 3.2 million images.
- The database consists of clean, full resolution images.
Summary & Methodology Analysis
The researchers aimed to solve the problem of organizing the explosion of image data on the Internet. Their primary methodology involved using WordNet, a large lexical database of English, to create a semantic backbone for organizing image collections. By using the synsets (a set of synonyms) within WordNet as the target categories, they provided a structured, hierarchical framework that allows for logical indexing of image content. This structure ensures that images are not just stored, but classified according to existing linguistic relationships.
To populate these categories with actual data, the team employed Amazon Mechanical Turk to execute a large-scale data collection scheme. This distributed approach allowed them to source and verify a massive volume of clean and full resolution images that correspond to specific synsets. By outsourcing the verification and curation tasks, they were able to handle the high throughput required to build a database of this magnitude. This crowdsourced approach effectively bridged the gap between raw internet data and a structured research dataset.
Despite the success of the implementation, the paper notes that constructing a database of this scale is difficult. The complexity of curating high-quality data at such a volume is a significant bottleneck. While the dataset currently features 3.2 million images across 5247 synsets, the paper does not specify the exact total time, compute, or financial cost required to reach this state, nor does it detail the specific error rates of the crowdsourcing process.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the main goal of the ImageNet project?
The project aims to effectively harness and organize the massive amount of image data available on the Internet.
Q2. How large is the ImageNet database currently?
The database contains 3.2 million images organized into 5247 synsets across 12 subtrees.
Q3. What role does WordNet play in this project?
WordNet serves as the semantic backbone, providing the hierarchical structure for organizing the images.
Q4. How did the researchers collect the images?
The researchers employed Amazon Mechanical Turk to execute their data collection scheme.
Q5. Are the images in the database compressed?
No, the database contains clean and full resolution images.
Q6. What are the primary technical limitations mentioned?
The paper states that the task of constructing a database of this large scale is inherently difficult.
Q7. Does the paper compare ImageNet to other existing databases?
The paper does not provide a comparison to other databases.
Q8. What is the total number of images stored in the database?
There are 3.2 million images.
Q9. How many synsets are currently populated in the database?
There are 5247 synsets currently populated.