A binary automatic segmentation algorithm for digital images of whales is presented. The algorithm is established by two cascade blocks in order to process an image and classify its pixels. The first block preprocess the information with a 1% grid subsampling over all pixels in each image, those samples are used to generate new indexes based in the RGB data channels which represents the colors of the whale. Because the ratio of a pixel whose is detected as whale and clutter does not tend to 1:1, an equal number random sampling selection is made in order to work with a homogeneous distribution in training process. An inference engine is the second block, this classify those pixels that are detected as whale according to its colorimetric features and samples obtained in the previous block. This block is realized by Principal Component Analysis (PCA), reducing the feature number for the model and increasing the data separability, and Logistic Regression for pixel classification. Our approach avoids the requirement of extensive computing power and specialized equipment, this allow the algorithm portability and implementation in consumer devices. The algorithm needs an annotated dataset because it is based in supervised learning strategy. In addition, this algorithm minimizes the required time to develop an automatic segmentation system around any object of interest in images.