Image fusion is the method of combining the features of different images into one to get a more informative or high-quality image. Among its various types, multi-modal image fusion is a crucial one where images obtained using sensors receptive to different light radiation are integrated into one final image. Infrared (IR) and Visible (VIS) Image Fusion (IVIF) is one such popular fusion technology. In IVIF, visible sensor produces clean texture and structure information, while it is sensitive to illumination and occlusion. IR sensor, though vulnerable to noise, captures salient targets that emit thermal radiation. The contrasting properties of the two images can be exploited by producing a fused image that both highlights the prominent target as well manifests detailed information. First, the acquired IR and VIS source images are each decomposed using the Gaussian blur filter into base (low-frequency) and detail (high-frequency) components. As opposed to the conventional way of concatenating the respective base and detailed components of the source images, a new technique of combinative concatenation has been performed providing a comprehensive set of 6 unique features to perform fusion. The proposed combinative concatenation is mathematically formulated, illustrating how cross-modal feature generation improves the retention of information and enhances modal complementarity. Weighted Sum (WS), Principal Component Analysis (PCA) and Laplacian Pyramid (LP) have been used for the fusion process. The 6 unique features extracted are fused in 20 different ways considering all combinations to provide fused results with different properties. Finally, a set of 4 statistical analysis methods are applied to identify the best fusion strategy. As a highlight, this paper has assessed these fusion strategies over live images captured using a Near-Infrared (NIR) and VIS camera depicting different illumination conditions (bright, dim and dark), and its effects over the fusion performance are assessed in comparison to fusion of similar images from an existing dataset.