FINC: An Efficient and Effective Optimization Method for Normalized Cut.

Area of Science:

• Computer Vision
• Machine Learning
• Data Mining

Background:

• Traditional normalized cut optimization involves computationally expensive multi-step processes like relaxation, eigendecomposition, and k-means clustering.
• These conventional methods suffer from performance issues as they do not directly address the original problem and are inefficient due to time-consuming post-processing steps.

Purpose of the Study:

• To develop a faster and more efficient optimization method for the normalized cut clustering problem.
• To address the limitations of existing three-step optimization techniques in terms of speed and direct problem-solving.

Main Methods:

• A novel one-step optimization approach is proposed, introducing an auxiliary variable that is alternatively updated with the cluster indicator matrix.
• Efficient methods for adjusting two key regularization parameters are also presented.

Main Results:

• The new method demonstrates faster convergence and monotonically decreases the normalized cut objective function.
• Extensive experimental results confirm the superior performance and speed of the proposed method compared to conventional techniques.
• The method solves the normalized cut problem in a single step, avoiding time-consuming eigendecomposition and k-means post-processing.

Conclusions:

• The proposed fast optimization method significantly accelerates the normalized cut clustering process.
• This approach offers a more efficient and effective solution for problems requiring normalized cut analysis, such as image segmentation and data clustering.