International business machines corporation (20240135675). DETECTING FINE-GRAINED SIMILARITY IN IMAGES simplified abstract
Contents
- 1 DETECTING FINE-GRAINED SIMILARITY IN IMAGES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DETECTING FINE-GRAINED SIMILARITY IN IMAGES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DETECTING FINE-GRAINED SIMILARITY IN IMAGES
Organization Name
international business machines corporation
Inventor(s)
Jian Long Sun of Langfang, Hebei (CN)
DETECTING FINE-GRAINED SIMILARITY IN IMAGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135675 titled 'DETECTING FINE-GRAINED SIMILARITY IN IMAGES
Simplified Explanation
The patent application describes a method for detecting fine-grained similarity in images by analyzing key areas and generating feature descriptors for comparison.
- Core area of search image determined by generating image salient map
- Feature descriptors generated from core area of search image
- Capsule vectors generated from keypoints of feature descriptors
- Comparison of capsule vectors between search image and dataset images
- Selection of images with fine-grained similarity based on bundled similarity score
Potential Applications
This technology could be applied in image search engines, content-based image retrieval systems, and image recognition software.
Problems Solved
This technology helps in identifying images with subtle similarities, which can be challenging for traditional image recognition algorithms.
Benefits
The method allows for more accurate and efficient image matching, enabling better search results and content organization.
Potential Commercial Applications
Commercial applications include visual search platforms, e-commerce product recommendations, and security systems for image verification.
Possible Prior Art
One possible prior art could be traditional image matching algorithms that rely on pixel-level comparisons rather than feature descriptors and capsule vectors.
What are the limitations of the proposed method in detecting fine-grained similarity in images?
The proposed method may have limitations in handling large datasets due to the computational complexity of generating feature descriptors and comparing capsule vectors.
How does this technology compare to existing image recognition systems in terms of accuracy and efficiency?
This technology offers improved accuracy in detecting fine-grained similarities but may require more computational resources compared to traditional image recognition systems.
Original Abstract Submitted
detecting fine-grained similarity in image includes determining a core area of a search image by generating an image salient map from a plurality of layers of the search image and determining a connected area based on the image salient map. feature descriptors are generated from the core area of the search image. a plurality of capsule vectors are generated from different ones of a plurality of keypoints of the feature descriptors. capsule vectors of the search image are compared with capsule vectors of each image of the dataset to generate a top-k matrix. similarity scores for the top-k matrix are calculated. one or more image of the dataset having fine-grained similarity with the search image are selected based a bundled similarity score for each image of the dataset. the bundled similarity score is a summation of the similarity scores of the image.
