Saliency Detection Using Sparse and Nonlinear Feature Representation

Abstract

An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection.

An Infrared and Visible Fusion Framework Based on a Novel Decomposition Method

“…There is a number of image fusion methods proposed for IR and VI images. Existing fusion methods can be classified into four categories: multi-scale transform (MST) [4][5][6][7][8][9][10][11][12][13][14][15], deep learning (DL) [16][17][18][19], sparse representation (SR) [20][21][22], and other methods [23][24][25]. In recent years, deep learning has proved superior and successful in many applications.…”