Overview of techniques for detecting object’s features and embedding them in multidimensional spaces

<ns3:p>It is evident that each object in the real world possesses unique properties. A subset of these characteristics can be readily described in quantitative terms. Examples of such features include the number of wheels in a vehicle, the floor area of a residential property, or the year of construction of a building. However, certain characteristics of objects exhibit a higher level of complexity. Examples of such features include object shape, color, and texture. These characteristics, frequently defined in terms of objects depicted in images, represent the primary characteristics that can be identified in real-world objects. The processing of these visual attributes has been the subject of scientific research for decades, and the literature on this topic is extensive. The objective of this article is to synthesize the existing methods for detecting object’s shape, color, and texture and embedding them in multidimensional spaces. By applying these methods, it is possible to represent the features of the object as points in multidimensional spaces. Such representations can be used to solve multicriteria optimization problems.</ns3:p>