View source for 18169281. IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING COMPUTER PROGRAM PRODUCT simplified abstract (KABUSHIKI KAISHA TOSHIBA)

=IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING COMPUTER PROGRAM PRODUCT=


==Organization Name==

[[:Category:KABUSHIKI KAISHA TOSHIBA|KABUSHIKI KAISHA TOSHIBA]]


[[Category:KABUSHIKI KAISHA TOSHIBA]]

==Inventor(s)==

[[:Category:Hiroo Saito of Kawasaki (JP)|Hiroo Saito of Kawasaki (JP)]][[Category:Hiroo Saito of Kawasaki (JP)]]

[[:Category:Tomoyuki Shibata of Kawasaki (JP)|Tomoyuki Shibata of Kawasaki (JP)]][[Category:Tomoyuki Shibata of Kawasaki (JP)]]

==IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING COMPUTER PROGRAM PRODUCT - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18169281 titled 'IMAGE PROCESSING APPARATUS, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING COMPUTER PROGRAM PRODUCT

==Simplified Explanation==

The patent application describes an image processing apparatus that includes acquisition, pseudo label estimation, and learning units to identify attributes of unlabeled training data.

* The acquisition unit acquires unlabeled training data with images lacking correct attribute labels.
* The pseudo label estimation unit estimates pseudo-labels for the attributes of the images based on an identification target region.
* The learning unit learns a model to identify the attributes using labeled training data with assigned pseudo-labels.

== Potential Applications ==
This technology could be applied in various fields such as image recognition, object detection, and pattern recognition.

== Problems Solved ==
This technology addresses the challenge of training models with unlabeled data by estimating pseudo-labels for attributes, improving the learning process.

== Benefits ==
The use of pseudo-labels for unlabeled data can enhance the accuracy and efficiency of attribute identification in images, leading to better performance of machine learning models.

== Potential Commercial Applications ==
Potential commercial applications of this technology include automated image tagging, content-based image retrieval, and quality control in manufacturing processes.

== Possible Prior Art ==
Prior art in the field of semi-supervised learning and active learning methods for training machine learning models with unlabeled data may be relevant to this technology.

== Unanswered Questions ==
1. How does the accuracy of attribute identification compare when using pseudo-labels versus manually labeled data?
2. What are the computational requirements for implementing this image processing apparatus in real-time applications?


==Original Abstract Submitted==

According to one embodiment, an image processing apparatus  includes one or more hardware processors configured to function as an acquisition unit A, a pseudo label estimation unit B, and a learning unit C. The acquisition unit A acquires unlabeled training data including an image to which a correct label of an attribute is unassigned. The pseudo-label estimation unit B estimates a pseudo-label, which is an estimation result of the attribute of the image of the unlabeled training data, based on an identification target region according to a type of the attribute to be identified by a first learning model  to be learned in the image of the unlabeled training data. The learning unit C learns the first learning model  identifying the attribute of the image by using first labeled training data with the pseudo-label being assigned to the image of the unlabeled training data.



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