Learning multi-objective binary features for image representation

Image representation is proven as a long-standing activity in computer vision. The rich context and large amount of information in images makes image recognition hard. So the image features must be extracted and learned correctly. Obtaining good image descriptors is greatly challenging. In recent years Learning Binary Features has been applied for many representation tasks of images, but it is shown to be efficient and effective just on face images. Therefore, designing a method that can be simultaneously successful in representing both texture and face images as well as other type of images is very important. Moreover, advanced binary feature methods need strong prior knowledge as they are hand-crafted. In order to address these problems, here a method is proposed that applies a pattern called Multi Cross Pattern (MCP) to extract the image features, which calculates the difference between all the pattern neighbor pixels and the pattern center pixel in a local square. In addition, a Multi-Objective Binary Feature method, named MOBF for short, is presented to address the aforementioned problems by the following four objectives: (1) maximize the variance of learned codes, (2) increase the information capacity of the binary codes, (3) prevent overfitting and (4) decrease the difference between binary codes of neighboring pixels. Experimental result on standard datasets like FERET, CMU-PIE, and KTH-TIPS show the superiority of MOBF descriptor on texture images as well as face images compared with other descriptors developed in literature for image representation.