Detecting changes of the same scene taken at different time instances is crucial and demanding for medical, remote sensing, infrastructure, agriculture, and planogram compliance applications. We propose a statistical-based approach by exploiting the linear relationship. Initially, region of interest is identified using a graph-cut-based technique followed by geometrical alignment via area-based registration. To perform statistical correlation, we adopt features such as block-wise average coefficient value of the first level of the discrete wavelet transform (DWT-LL1) and the map obtained using hybrid saliency approaches. In the former approach, Pearson’s correlation measure is calculated for the DWT-LL1, and in the latter, PCC has been calculated using the saliency value. Change has been detected using optimal PCC value while minimizing the error rate. Experimental results on datasets reveal that saliency feature and DWT-LL1 perform better for normal and noise corrupted images, respectively. The efficiency of the proposed method is validated by user study with average mean opinion score of 70%. Hybrid saliency-based change detection gives 92.9% of correct classification and hence useful for the vision-based applications like damage detection in a car.