A Multi-feature Optimization Approach to Object-Based Image Classification

This paper proposes a novel approach for the construction and use of multi-feature spaces in image classification. The proposed technique combines low-level descriptors and defines suitable metrics. It aims at representing and measuring similarity between semantically meaningful objects within the defined multi-feature space. The approach finds the best linear combination of predefined visual descriptor metrics using a Multi-Objective Optimization technique. The obtained metric is then used to fuse multiple non-linear descriptors is be achieved and applied in image classification.

• Type: Conference
• Year: 2006
• Where: CIVR
• Authors: Qianni Zhang, Ebroul Izquierdo
• Keywords: CIVR 2006, Combines Low-level Descriptors, Image Analysis, Image Classification, Multi-feature Space