An image display apparatus comprising: a display; a memory storing instructions; and a processor configured to execute the instructions stored in the memory, which when executed cause the processor to: control the display to output video content; receive a first user input configured to select a frame from among a plurality of frames constituting the video content; receive a second user input configured to select an object included within the frame; recognize the object; determine a plurality of candidate frames which are recognized as including the selected object from among the plurality of frames, based on there being a high similarity between the selected object and one or more objects included within each of the plurality of frames; determine a recommendation score of each of the plurality of candidate frames by using a learning model including one or more neural networks, the learning model determining the recommendation score of each of the plurality of candidate frames, based on at least one of a size of the selected object, a brightness of the selected object, or a focus of the selected object in a plurality of candidate frames, wherein the recommendation score corresponds to the at least one of the size of the selected object, the brightness of the selected object, or the amount that the selected object is in focus in the plurality of candidate frames; determine a recommended frame that includes an optimal image of the selected object from the plurality of candidate frames, wherein the recommended frame is the candidate frame with the highest recommendation score from the recommendation scores of the plurality of candidate frames; and control the display to output the recommended frame that includes the optimal image of the selected object. a display; a memory storing instructions; and a processor configured to execute the instructions stored in the memory, which when executed cause the processor to: control the display to output video content; receive a first user input configured to select a frame from among a plurality of frames constituting the video content; receive a second user input configured to select an object included within the frame; recognize the object; determine a plurality of candidate frames which are recognized as including the selected object from among the plurality of frames, based on there being a high similarity between the selected object and one or more objects included within each of the plurality of frames; determine a recommendation score of each of the plurality of candidate frames by using a learning model including one or more neural networks, the learning model determining the recommendation score of each of the plurality of candidate frames, based on at least one of a size of the selected object, a brightness of the selected object, or a focus of the selected object in a plurality of candidate frames, wherein the recommendation score corresponds to the at least one of the size of the selected object, the brightness of the selected object, or the amount that the selected object is in focus in the plurality of candidate frames; determine a recommended frame that includes an optimal image of the selected object from the plurality of candidate frames, wherein the recommended frame is the candidate frame with the highest recommendation score from the recommendation scores of the plurality of candidate frames; and control the display to output the recommended frame that includes the optimal image of the selected object. control the display to output video content; receive a first user input configured to select a frame from among a plurality of frames constituting the video content; receive a second user input configured to select an object included within the frame; recognize the object; determine a plurality of candidate frames which are recognized as including the selected object from among the plurality of frames, based on there being a high similarity between the selected object and one or more objects included within each of the plurality of frames; determine a recommendation score of each of the plurality of candidate frames by using a learning model including one or more neural networks, the learning model determining the recommendation score of each of the plurality of candidate frames, based on at least one of a size of the selected object, a brightness of the selected object, or a focus of the selected object in a plurality of candidate frames, wherein the recommendation score corresponds to the at least one of the size of the selected object, the brightness of the selected object, or the amount that the selected object is in focus in the plurality of candidate frames; determine a recommended frame that includes an optimal image of the selected object from the plurality of candidate frames, wherein the recommended frame is the candidate frame with the highest recommendation score from the recommendation scores of the plurality of candidate frames; and control the display to output the recommended frame that includes the optimal image of the selected object.