Generic object recognition using automatic region extraction and dimensional feature integration utilizing multiple kernel learn

Recently, in generic object recognition research, a classification technique based on integration of image features is garnering much attention. However, with a classifying technique using feature integration, there are some features that may cause incorrect recognition of objects and a large amount of noise that causes a degradation in the recognition accuracy of image data. In this paper, we propose feature selection in an object area that is restricted by removing its background region, and multiple kernel learning (MKL) to weight each dimension, as well as the features themselves. This enables accurate and effective weighting since the weight is computed for each dimension using the selected feature. Experimental results indicate the validity of automatic feature selection. Classification performance is improved by using a background removing technique that utilizes saliency maps and graph cuts, and each dimensional weighting method using MKL.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Toru Nakashika, Akira Suga, Tetsuya Takiguchi, Yasuo Ariki
• Keywords: Dimensional Weighting Method, Feature Selection, Generic Object Recognition, ICASSP 2011, Signal Processing