Nowadays, the algorithms most used for object recognition are based on a constructed database or on training and learning processes with many samples, allowing robots to effectively perform object recognition. If objects in a home environment do not appear in a database, a system cannot recognize and segment items from household goods. In this study, we proposed an algorithm to reduce the processing complexity of object recognition that combines a depth image, object segmentation, and model construction with the GrabCut algorithm, and uses a hierarchical design for the segmentation of items. This algorithm uses the depth image to find the approximate locations and sizes of multiple objects in a coarse layer, then it uses GrabCut as a fine segmentation technology to segment the edges of objects and construct the models. First, we use the inputs of binocular vision to generate an anaglyph image, which is used as the base to perceive the environment’s 3D information. At the same time, the too distant background is filtered, then histogram segmentation of the analysis image is used to partition each object. Next, GrabCut is used to find a convergent partition on the masking image to generate complete object edges. Finally, the scale-invariant feature transform (SIFT) is used for the extraction and recognition of feature points, and the database is updated.