Vision Saliency Feature Extraction Based on Multi-scale Tensor Region Covariance

• Published in: Information Retrieval Vol. 10390; pp. 185 - 197
• Main Authors: Wang, Shimin, Wang, Mingwen, Ye, Jihua, Jie, Anquan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Multi-scale transform; Region covariance; Saliency feature; Tensor space
• ISBN: 3319686984; 9783319686981
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-68699-8_15

In the process of extracting image saliency features by using regional covariance, the low-level higher-order data are dealt with by vectorization, however, the structure of the data (color, intensity, direction) may be lost in the process, leading to a poorer representation and overall performance degradation. In this paper we introduce an approach for sparse representation of region covariance that will preserve the inherent structure of the image. This approach firstly calculates the image low-level data (color, intensity, direction), and then uses multi-scale transform to extract the multi-scale features for constructing tensor space, at last by using tensor sparse coding the image bottom features are extracted from region covariance. In the paper, it compares the experimental results with the commonly used feature extraction algorithms’ results. The experimental results show that the proposed algorithm is closer to the actual boundary of the object and achieving better results.