Patent Publication Number: US-9886650-B2

Title: Method and device for determining similarity between sequences

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority from Korean Patent Application No. 10-2015-0033246, filed on Mar. 10, 2015, in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference herein in its entirety. 
     BACKGROUND 
     1. Field 
     Methods and apparatuses consistent with exemplary embodiments relate to determining a similarity between sequences in an image. 
     2. Description of the Related Art 
     As portable devices become popular and video sharing sites become more actively used, sharing of images by users has increased. Users may share images, which may be copied, edited, and thus, reproduced, with each other, and each reproduced image becomes an image that is similar, but not identical to, an original image. The similar image may include an image having a resolution that is different from that of the original image, an image to which a border is added due to a size difference from that of the original image, an image obtained by performing image processing such as an increase in a contrast ratio of the original image, an image to which a subtitle or a logo is added, or an image whose number of total frames including same frames are different from that of the original image. If the number of similar images is increased, it may be difficult to determine a similarity between an original image and a similar image, and an unnecessary search result may be provided to a user. 
     SUMMARY 
     Exemplary embodiments may address at least the above problems and/or disadvantages and other disadvantages not described above. Also, the exemplary embodiments are not required to overcome the disadvantages described above, and may not overcome any of the problems described above. 
     One or more exemplary embodiments provide a method and a device for determining a similarity between sequences in an image. 
     According to an aspect of an exemplary embodiment, there is provided a method of determining similarity between sequences, the method including obtaining a first sequence of frames and a second sequence of frames, determining a descriptor of a first frame of the first sequence based on luma information of blocks in the first frame, the descriptor of the first frame including luma difference information that is determined based on the luma information of the blocks in the first frame, determining a descriptor of a second frame of the second sequence based on luma information of blocks in the second frame, the descriptor of the second frame including luma difference information that is determined based on the luma information of the blocks in the second frame, and determining a similarity between the first sequence and the second sequence based on the descriptor of the first frame and the descriptor of the second frame. 
     The determining the similarity may include matching the first frame with the second frame based on the descriptor of the first frame and the descriptor of the second frame, and determining a section in the second sequence that matches a section in the first sequence as a section similar to the section in the first sequence, based on the matched first frame and second frame. 
     The matching may include determining whether a Hamming distance between the descriptor of the first frame and the descriptor of the second frame is shortest, and matching the first frame with the second frame in response to the determining that the Hamming distance between the descriptor of the first frame and the descriptor of the second frame is shortest. 
     The determining the section in the second sequence may include obtaining a matching vector representing a matching relation between the matched first frame and second frame, determining a representative matching vector based on the obtained matching vector, and determining the section in the second sequence that matches the section in the first sequence as the section similar to the section in the first sequence, based on the representative matching vector. 
     The method may further include determining whether the matching vector of the first frame is not included in a range based on the representative matching vector, and re-matching the first frame with a frame in the second sequence within the range based on the representative matching vector, in response to the determining that the matching vector of the first frame is not included in the range based on the representative matching vector. 
     The method may further include extracting one or more first frames from the frames of the first sequence based on a frame rate, the first frame being one of the one or more first frames, and extracting one or more second frames from the frames of the second sequence based on the frame rate, the second frame being one of the one or more second frames. 
     The determining the descriptor of the first frame or the second frame may include determining whether a number of pixels having a same pixel value at a same location in the frames of the first sequence or the second sequence, and in a row or a column of the first frame or the second frame, is greater than or equal to a number, and determining the descriptor based on the luma information of the blocks in the first frame or the second frame other than the row or the column of the first frame or the second frame, in response to the determining that the number of pixels having the same pixel value at the same location in the frames of the first sequence or the second sequence, and in the row or the column of the first frame or the second frame, is greater than or equal to the number. 
     The method may further include determining a descriptor of the first sequence based on the descriptor of the first frame, and determining a descriptor of the second sequence based on the descriptor of the second frame, and the determining the similarity may include determining the similarity based on a comparison of the descriptor of the first sequence to the descriptor of the second sequence. 
     The determining the descriptor of the first sequence may include converting the descriptor of the first frame into the descriptor of the first sequence based on codes, and the determining the descriptor of the second sequence may include converting the descriptor of the second frame into the descriptor of the second sequence based on the codes. 
     A non-transitory computer-readable recording storage medium may store a program to cause a computer to perform the method. 
     According to an aspect of another exemplary embodiment, there is provided a device for determining similarity between sequences, the device including a sequence obtainer configured to obtain a first sequence of frames and a second sequence of frames, and a descriptor determiner configured to determine a descriptor of a first frame of the first sequence based on luma information of blocks in the first frame, the descriptor of the first frame including luma difference information that is determined based on the luma information of the blocks in the first frame, and determine a descriptor of a second frame of the second sequence based on luma information of blocks in the second frame, the descriptor of the second frame including luma difference information that is determined based on the luma information of the blocks in the second frame. The device further includes a similarity determiner configured to determine a similarity between the first sequence and the second sequence based on the descriptor of the first frame and the descriptor of the second frame. 
     The similarity determiner may be further configured to match the first frame with the second frame based on the descriptor of the first frame and the descriptor of the second frame, and determine a section in the second sequence that matches a section in the first sequence as a section similar to the section in the first sequence, based on the matched first frame and second frame. 
     The similarity determiner may be further configured to determine whether a Hamming distance between the descriptor of the first frame and the descriptor of the second frame is shortest, and match the first frame with the second frame in response to the similarity determiner determining that the Hamming distance between the descriptor of the first frame and the descriptor of the second frame is shortest. 
     The similarity determiner may be further configured to obtain a matching vector representing a matching relation between the matched first frame and second frame, determine a representative matching vector based on the obtained matching vector, and determine the section in the second sequence that matches the section in the first sequence as the section similar to the section in the first sequence, based on the representative matching vector. 
     The similarity determiner may be further configured to determine whether the matching vector of the first frame is not included in a range based on the representative matching vector, and re-match the first frame with a frame in the second sequence within the range based on the representative matching vector, in response to the similarity determiner determining that the matching vector of the first frame is not included in the range based on the representative matching vector. 
     The sequence obtainer may be further configured to extract one or more first frames from the frames of the first sequence based on a frame rate the first frame being one of the one or more first frames, and extract one or more second frames from the frames of the second sequence based on the frame rate, the second frame being one of the one or more second frames. 
     The descriptor determiner may be further configured to determine whether a number of pixels having a same pixel value at a same location in the frames of the first sequence or the second sequence, and in a row or a column of the first frame or the second frame, is greater than or equal to a number, and determine the descriptor based on the luma information of the blocks in the first frame or the second frame other than the row or the column of the first frame or the second frame, in response to the descriptor determiner determining that the number of pixels having the same pixel value at the same location in the frames of the first sequence or the second sequence, and in the row or the column of the first frame or the second frame, is greater than or equal to the number. 
     The descriptor determiner may be further configured to determine a descriptor of the first sequence based on the descriptor of the first frame, and determine a descriptor of the second sequence based on the descriptor of the second frame, and the similarity determiner may be further configured to determine the similarity based on a comparison of the descriptor of the first sequence to the descriptor of the second sequence. 
     The descriptor determiner may be further configured to convert the descriptor of the first frame into the descriptor of the first sequence based on codes, and convert the descriptor of the second frame into the descriptor of the second sequence based on the codes. 
     The descriptor determiner may be further configured to convert the descriptor of the first frame into an index number of a code that is most similar to the descriptor of the first frame, among the codes, and generate a histogram of index numbers based on the index number as the descriptor of the first sequence. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and/or other aspects will be more apparent by describing exemplary embodiments with reference to the accompanying drawings, in which: 
         FIG. 1  is a block diagram of a device for determining a similarity between sequences according to an exemplary embodiment; 
         FIG. 2  is a diagram illustrating a process of determining a similarity between sequences, which is performed by the device, according to an exemplary embodiment; 
         FIG. 3  is a diagram illustrating an overlapping section between sequences that is determined by the device, according to an exemplary embodiment; 
         FIGS. 4A, 4B, 4C, and 4D  are diagrams illustrating a process of determining a descriptor, which is performed by the device, according to an exemplary embodiment; 
         FIG. 5  is a diagram illustrating a process of matching frames with each other, which is performed by the device, according to an exemplary embodiment; 
         FIGS. 6, 7A, 7B, 8A, 8B, and 9  are diagrams illustrating a process of determining a similarity between sequences by matching frames with each other, which is performed by the device, according to an exemplary embodiment; 
         FIGS. 10A, 10B, and 10C  are diagrams illustrating a process of pre-processing a sequence to determine a descriptor, which is performed by the device, according to an exemplary embodiment; 
         FIG. 11  is a diagram illustrating a process of determining a similarity between sequences, which is performed by the device, according to another exemplary embodiment; 
         FIGS. 12A, 12B, 13A, and 13B  are diagrams illustrating a process of determining a descriptor of a sequence, which is performed by the device, according to another exemplary embodiment; 
         FIGS. 14A, 14B, and 14C  are diagrams illustrating a process of determining a descriptor, which is performed by the device, according to another exemplary embodiment; 
         FIGS. 15A, 15B, and 15C  are diagrams illustrating a process of determining a descriptor, which is performed by the device, according to another exemplary embodiment; 
         FIG. 16  is a flowchart of a method of determining a similarity between sequences, which is performed by the device, according to an exemplary embodiment; and 
         FIG. 17  is a flowchart of a method of determining a similarity between sequences, which is performed by the device, according to another exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary embodiments are described in greater detail below with reference to the accompanying drawings. 
     In the following description, like drawing reference numerals are used for like elements, even in different drawings. The matters defined in the description, such as detailed construction and elements, are provided to assist in a comprehensive understanding of the exemplary embodiments. However, it is apparent that the exemplary embodiments can be practiced without those specifically defined matters. Also, well-known functions or constructions may not be described in detail because they would obscure the description with unnecessary detail. 
     It will be understood that the terms “comprises” and/or “comprising” used herein specify the presence of stated features or components, but do not preclude the presence or addition of one or more other features or components. In addition, the terms such as “unit,” “-er (-or),” and “module” described in the specification refer to an element for performing at least one function or operation, and may be implemented in hardware, software, or the combination of hardware and software. 
       FIG. 1  is a block diagram of a device  100  for determining a similarity between sequences according to an exemplary embodiment. 
     According to an exemplary embodiment, the device  100  includes a sequence obtainer  110 , a descriptor determiner  120 , and a similarity determiner  130 . Each element of the device  100  will be described after an example of determining a similarity between sequences, which is performed by the device  100 , is described with reference to  FIGS. 2 and 3 . 
       FIG. 2  is a diagram illustrating a process of determining a similarity between sequences, which is performed by the device  100 , according to an exemplary embodiment. 
     According to an exemplary embodiment, the device  100  may compare frames included in a sequence to frames included in another sequence with respect to a plurality of sequences, and thus, determine a similarity between the sequences. 
     A sequence is a part or whole of an image, and may be a group of consecutive frames that constitute a part or whole of a plurality of frames included in the image. The device  100  may obtain a plurality of sequences from an image or images that are different from each other. For example, the device  100  may obtain a first sequence that includes a first frame and a second sequence that includes a second frame. 
     The device  100  may determine a descriptor representing color information about a frame, with respect to frames included in the obtained plurality of sequences, and compare frames, included in sequences from each other, to each other by using the determined descriptor. Color information about a frame is information about values of pixels included in the frame, and may be information that includes intensity information, luma (luminance) information, or chroma information for each color channel. 
     A descriptor may be a descriptor of a frame that represents color information about the frame, or a descriptor of a sequence that represents color information about the sequence. A descriptor of a sequence may be determined by using descriptors of frames included in the sequence. 
     The device  100  may determine a descriptor with respect to a frame. For example, the device  10  may determine a descriptor of a first frame based on luma information about a plurality of blocks in the first frame included in the first sequence, and determine a descriptor of a second frame based on luma information about a plurality of blocks in the second frame included in the second sequence. 
     In detail, the device  100  may determine a luma difference between a plurality of blocks based on luma information about the plurality of blocks that constitute a frame, and determine a descriptor of a frame that includes information about the luma difference between the plurality of blocks according to a predetermined order. A detailed process of determining a descriptor will be described with reference to  FIGS. 4A through 4D . 
     The device  100  may determine a similarity between sequences by using the determined descriptors. For example, the device  100  may determine a similarity between the first sequence and the second sequence by using the descriptor of the first frame and the descriptor of the second frame. The device  100  may detect a section in the second sequence that is determined as a section similar to a section in the first sequence, from sections that include one or more frames in the second sequence, by using the descriptor of the first frame and the descriptor of the second frame. 
     The device  100  may determine or detect whether sequences are similar to each other or determine sections similar to or overlapping each other between the sequences, by matching frames included in sequences different from each other by using the determined descriptors. A method of matching frames with each other will be described in detail with reference to  FIGS. 5 through 9 . 
       FIG. 3  is a diagram illustrating an overlapping section between sequences that is determined by the device  100 , according to an exemplary embodiment. 
     Referring to  FIG. 3 , a first sequence that includes a frame  1  and a frame  2 , and a second sequence that includes frames  1  through  4 . 
     The device  100  may determine descriptors of frames included in each sequence, and compare frames included in the first sequence to frames included in the second sequence by using the determined descriptors of each frame. 
     The device  100  may match one of the frames included in the first sequence with one of the frames included in the second sequence by comparing the frames to each other. The device  100  may determine a section in the second sequence, which matches a section in the first sequence, and thus, is a similar section with respect to the section in the first sequence, based on the matching between the frames. 
     For example, in  FIG. 3 , the frame  1  in the first sequence may match the frame  3  in the second sequence, and the frame  2  in the second sequence may match the frame  4  in the second sequence. Accordingly, the device  100  may determine a section that includes the frames  3  and  4  in the second sequence as a section similar to a section that includes the frames  1  and  2  in the first sequence. 
     The device  100  may improve efficiency in data management, by quickly determining whether sequences are similar to each other or determining similar sections between sequences by using descriptors, and provide information about a similarity between images to a user. Additionally, the device  100  may synchronize sequences with each other, and thus, provide an easy and quick image editing method. 
     Referring back to  FIG. 1 , the sequence obtainer  110  is an element for obtaining a plurality of sequences, and may obtain a plurality of sequences from an image stored in the device  100  or an external apparatus. The sequence obtainer  110  may obtain a plurality of sequences from an image or images different from each other. The sequence obtainer  110  may transmit the obtained plurality of sequences to the descriptor determiner  120 . 
     The sequence obtainer  110  may extract one or more frames, from a plurality of frames included in the obtained plurality of sequences, as key frames and determine a similarity between the plurality of frames by using only the key frames obtained by the extracting. 
     For example, if frames rates of the obtained plurality of sequences are different from each other, a matching relation between the plurality of frames may be complicated. Additionally, in a case of a sequence having a very high frame rate, too many operations may be used to match frames with each other. 
     Accordingly, the sequence obtainer  110  may extract key frames from the obtained sequences according to a predetermined frame rate, to easily represent a matching relation between frames and effectively increase an operation speed. In detail, the sequence obtainer  110  may extract one or more frames included in the first sequence as one or more key frames in the first sequence according to a predetermined frame rate, and extract one or more frames included in the second sequence as one or more key frames in the second sequence according to the frame rate. 
     The sequence obtainer  110  may transmit the extracted one or more key frames to the descriptor determiner  120 . 
     The descriptor determiner  120  is an element for determining a descriptor representing color information about a frame or a sequence. The descriptor determiner  120  may determine a descriptor of a frame that represents color information about the frame or a descriptor of a sequence that represents color information about the sequence. The descriptor determiner  120  may determine a descriptor of a sequence by using descriptors of frames included in the sequence. 
     The descriptor determiner  120  may determine a descriptor of a frame by using color information about pixels included in the frame. For example, the descriptor determiner  120  may determine a descriptor of a first frame based on luma information about pixels included in the first frame, and determine a descriptor of a second frame based on luma information about pixels included in the second frame. 
     For example, the descriptor determiner  120  may divide the first frame into a plurality of blocks that have a same size and include a plurality of pixels. The descriptor determiner  120  may determine luma information about each block by using luma values of pixels included each block. For example, luma information about a block may be an average luma value of the block. 
     The descriptor determiner  120  may compare the determined luma information about the respective blocks to each other, and thus, determine information about a luma difference between blocks. The information about the luma difference between blocks may be information obtained by numeralizing a value of a difference between average luma values of the respective blocks according to a predetermined reference section. For example, the information about the luma difference may be information expressing a value of a difference between average luma values of two blocks in a 2-bit binary code. The descriptor determiner  120  may determine a descriptor of the first frame that is expressed in a 2-bit binary code based on information about a luma difference between the determined plurality of blocks. 
     Additionally, the descriptor determiner  120  may also determine a descriptor of the first frame based on color information or chroma information about pixels included in the first frame. 
     The descriptor determiner  120  may determine a descriptor of a frame with respect to all frames or key frames in the first sequence and the second sequence received from the sequence obtainer  110 . 
     The descriptor determiner  120  may determine a descriptor of the first sequence based on descriptors of a plurality of frames in the first sequence including the descriptor of the first frame, and determine a descriptor of the second sequence based on descriptors of a plurality of frames in the second sequence including the descriptor of the second frame. A process of determining a descriptor of a sequence will be described in detail with reference to  FIGS. 11 through 13 . 
     The descriptor determiner  120  may transmit the descriptor of the first frame and the descriptor of the second frame to the similarity determiner  130 . Alternately, the descriptor determiner  120  may transmit the descriptor of the first sequence and the descriptor of the second sequence to the similarity determiner  130 . 
     The similarity determiner  130  may determine a similarity between sequences by using the descriptors received from the descriptor determiner  120 . The similarity determiner  130  may determine a similarity between the first sequence and the second sequence by using the descriptor of the first frame and the descriptor of the second frame that are received from the descriptor determiner  120 . 
     In detail, the similarity determiner  130  may match the first frame with the second frame by using the descriptor of the first frame and the descriptor of the second frame. The matching of frames may include determination of two frames, which are included in sequences different from each other and are determined as being most similar to each other. For example, a first frame in the first sequence may match the second frame in the second sequence. 
     The similarity determiner  130  may determine frames having a shortest matching distance therebetween as most similar frames, and match the frames with each other. For example, the similarity determiner  130  may determine a matching distance between frames by calculating a Hamming distance between descriptors of the frames. 
     A Hamming distance is a number of different characters located in a same position in two code strings having a same number of bits. For example, because a third bit and a fourth bit are different between a first code “00011” and a second code “00101,” a Hamming distance between the first code and the second code is 2. 
     Equation 1 for calculating a Hamming distance between an ath frame in a first sequence and a bth frame in a second sequence is as follows: 
     
       
         
           
             
               
                 
                   
                     d 
                     ⁡ 
                     
                       ( 
                       
                         a 
                         , 
                         b 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       i 
                     
                     ⁢ 
                     
                       | 
                       
                         
                           Ia 
                           ⁡ 
                           
                             ( 
                             i 
                             ) 
                           
                         
                         - 
                         
                           Ib 
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                             ( 
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                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
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     In Equation 1, a may represent an index of a frame included in the first sequence, and b may represent an index of a frame in a second sequence. Ia(i) may represent an ith bit value of a descriptor of the ath frame, and Ib(i) may represent an ith bit value of a descriptor of the bth frame. 
     The similarity determiner  130  may calculate a Hamming distance between the descriptor of the first frame in the first sequence and descriptors of all frames in the second sequence and match a frame in the second sequence, which has a shortest Hamming distance from the first frame, with the first frame. For example, if a Hamming distance between the descriptor of the first frame and the descriptor of the second frame is shortest, the similarity determiner  130  may match the first frame with the second frame. 
     The similarity determiner  130  may represent a matching relation between frames, which is obtained by using descriptors, as a matching vector. The matching vector may be a value of a difference between an index of the first frame and an index of the second frame. For example, if the index of the first frame in the first sequence is 1 and the index of the second frame in the first sequence is 5, the matching vector may be expressed as 4(5−1=4). 
     The similarity determiner  130  may determine a section in the second sequence, which matches a predetermined section in the first sequence as a similar section, by using the determined matching vector. Alternately, the similarity determiner  130  may determine a representative matching vector to speed up the similarity determination. For example, the similarity determiner  130  may determine a vector having a highest frequency, among matching vectors between sequences that are different from each other, as a representative matching vector. Alternately, the similarity determiner  130  may determine a vector having an average value, among the matching vectors between the sequences that are different from each other, as a representative matching vector. However, the determining of a representative matching vector is not limited thereto, and a representative matching vector may be determined by using various methods. 
     The similarity determiner  130  may determine a matching having a matching vector that is not similar to the representative matching vector, among matchings, as noise, by using the determined representative matching vector, and thus, remove the matching determined as noise. The similarity determiner  130  may perform an additional matching on frames whose matching was removed. In detail, the similarity determiner  130  may perform the additional matching on frames whose matching was removed so that a matching vector of the additional matching is similar to the representative matching vector. For example, the similarity determiner  130  may match the frames with each other within a range according to the representative matching vector. 
     The similarity determiner  130  may determine a section in the second sequence that matches a section in the first sequence as a similar section, based on matching vectors of the additional matching. 
     Additionally, the similarity determiner  130  may receive a descriptor of the first sequence and a descriptor of the second sequence from the descriptor determiner  120 , and thus, determine whether the first sequence is similar to the second sequence. 
       FIGS. 4A, 4B, 4C, and 4D  are diagrams illustrating a process of determining a descriptor, which is performed by the device  100 , according to an exemplary embodiment. 
     The device  100  may determine a descriptor of a frame by using luma information about frames included in an obtained sequence. For example, a descriptor of a frame may be information about a difference between luma values at locations in the frame. An example of a process of determining a descriptor of a first frame included in a first sequence is described with reference to  FIGS. 4A through 4D . 
     Referring to  FIG. 4A , the device  100  may divide the first frame into a plurality of blocks that have a same size and include a plurality of pixels. For example, the device may divide the first frame into 16 blocks having a same size. A number shown on a block refers to an index of the block. 
     Referring to  FIG. 4B , the device  100  may determine luma information about each block by using a luma value of pixels included in each block. For example, luma information about a first block  401  may be determined as  130 , which is an average luma value of pixels included in the first block  401 . Additionally, luma information about a second block  402  may be determined as 81. 
     Referring to  FIG. 4C , the device  100  may determine information about a luma difference between blocks, by using luma information about the blocks. For example, the device  100  may determine luma difference information  403  by using a value of a difference between an average luma value of the first block  401  and an average luma value of the second block  402 . The luma difference information may be information obtained by numeralizing a value of a difference between values of the luma information about blocks according to a predetermined reference section. 
     In detail, the device  100  may determine luma difference information Pij as shown in Equation 2, according to sizes of luma values included into luma information about two blocks that are to be compared to each other. 
     
       
         
           
             
               
                 
                   Pij 
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                             0 
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                                 Li 
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                                 Li 
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                   [ 
                   
                     Equation 
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                     2 
                   
                   ] 
                 
               
             
           
         
       
     
     In Equation 2, Pij represents information about a luma difference between an ith block and a jth block. Li represents luma information about the ith block, and Lj represents luma information about the jth block. T may be a predetermined constant for setting a predetermined reference section. For example, T may be 20. 
     Because a value of a difference between luma information about the first block  410  and the second block  402  is 49, and thus, greater than 20, the device  100  may determine the luma difference information  403  with respect to the first block  401  and the second block  402  as 2. Additionally, the device  100  may continue to compare the first block  401  to other blocks, and thus, determine information about a luma difference between blocks. The device  100  may arrange the determined information about the luma difference between the blocks according to a predetermined order. In  FIG. 4C , if 16 blocks are included in a first frame, the device  100  may determine information about a luma difference with respect to the first frame that includes 120 numbers in total. 
     The device  100  may convert the determined luma difference information into a binary code, and thus, determine a descriptor of the first frame. For example, luma difference information 0 may be converted into 00, luma difference information 1 may be converted into 01, and luma difference information 2 may be converted into 11 as in a first block  404  of the descriptor. For example, in  FIG. 4D , the device  100  may determine a 240-bit binary descriptor with respect to the first frame. 
     The device  100  may determine a descriptor of a frame by performing a same process on all frames in an obtained sequence. The device  100  may match frames with each other by using the determined descriptors of the frames. 
       FIG. 5  is a diagram illustrating a process of matching frames with each other, which is performed by the device  100 , according to an exemplary embodiment. 
     The device  100  may match frames by using the determined descriptors, to determine a similarity between sequences. For example, the device  100  may match the first frame in the first sequence with a frame that is most similar to the first frame among frames included in the second sequence.  FIG. 5  shows a process of matching five frames included in the first sequence with five frames included in the second sequence. 
     The device  100  may use a descriptor of a frame to determine a frame that is most similar to the first frame. 
     In detail, the device  100  may determine a matching distance “Dab” between a first frame in the first sequence and all frames in the second sequence, and thus, determine frames, whose matching distance from the first frame is shortest, as similar frames. a may represent an index of a frame in the first sequence, and b may represent an index of a frame in the second sequence. The device  100  may calculate a Hamming distance between descriptors of frames, and thus, determine a matching distance between the frames. 
     Referring to  FIG. 5 , the device  100  may calculate a matching distance between the first frame that is a frame  1  in the first sequence and a frame  1  in the second sequence as D 11 =105. Additionally, the device  100  may calculate matching distances between the first frame in the first sequence and frames  1  through  5  in the second sequence respectively as D 12 =80, D 13 =5, D 14 =40, and D 15 =53. The device  100  may match the first frame that is the frame  1  in the first sequence with the frame  3  in the second sequence that has a shortest matching distance D 13 =5 from the frame  1  in the first sequence. 
     The device  100  may match all frames in the first sequence respectively with a frame among frames in the second sequence by performing the same process. 
       FIGS. 6, 7A, 7B, 8A, and 8B  are diagrams illustrating a process of determining a similarity between sequences by matching frames with each other, which is performed by the device  100 , according to an exemplary embodiment. 
       FIG. 6  illustrates matching of all frames in a first sequence with frames in a second sequence, according to the process of matching frames with each other that is described with reference to  FIG. 5 . 
     Referring to  FIG. 6 , the first sequence may include 15 frames, and the second sequence may include 17 frames. A frame  1  in the first sequence may match a frame  3  in the second sequence and a frame  2  in the first sequence may match a frame  4  in the second sequence, according to the process of the matching described with reference to  FIG. 5 . 
     The device  100  may represent a matching relation between frames as a matching vector. For example, the matching vector may be a value of a difference between an index of a frame in the first sequence and an index of a frame in the second sequence. For example, the device  100  may represent a matching vector between the frame  1  in the first sequence and the frame  3  in the second sequence as 2(3−1=2), and represent a matching vector between the frame  2  in the first sequence and the frame  4  in the second sequence as 2(4−2=2). 
     The device  100  may determine a representative matching vector among the determined matching vectors. For example, the device  100  may determine a vector having a highest frequency, among the determined matching vectors, as a representative matching vector. Referring to  FIG. 7A , matching vectors determined as a representative matching vector, among the matching vectors determined in the process described with reference to  FIG. 6 , are shown in a solid line. Other matching vectors than the representative matching vector are shown in a dotted line. 
     The device  100  may determine matchings having a matching vector that is not similar to the representative matching vector, among matchings between frames, as noise, by using the determined representative matching vector, and thus, remove the matchings. For example, the device  100  may set a range with reference to the representative matching vector, remove matchings having a matching vector that is greater or less than the representative matching vector by 2, and extract only matchings, which have a matching vector that is equal to, or greater or less than the representative matching vector by 1, from the matchings. Referring to  FIG. 7B , matching vectors included in a range according to the representative matching vector are shown. 
     In  FIG. 7B , both frames  12  and  13  in the first sequence match a frame  14  in the second sequence. Accordingly, redundant matchings may occur. Additionally, because a matching vector of the frame  12  and a matching vector of the frame  13  in the first sequence are included in a range according to the representative matching vector, the matching vectors thereof are not removed. Accordingly, the device  100  may remove a matching from the redundant matchings. For example, the device  100  may remove a matching having a longer matching distance between frames than another matching from redundant matchings. 
     The device  100  may perform an additional matching on frames that are not matched as a matching thereof was removed. For example, referring to  FIG. 8A , as matchings are removed, frames that are not matched with each other occur. For example, frames  3 ,  4 ,  5 ,  9 ,  10 ,  13 , and  15  in the first sequence may be frames that are not matched with each other. 
     The device  100  may extract a non-matching section  810 , in which frames that are not matched with each other are consecutively present in the first sequence, from the first sequence, and thus, determine whether to perform an additional matching on the frames in the non-matching section  810 . 
     For example, if a number of frames included in the non-matching section  810  in the first sequence is greater than a predetermined number, the device  100  may determine that a section similar to the non-matching section  810  in the first sequence is not present in the second sequence, and thus, determine that the frames are not to be further performed. Additionally, if a number of the frames included in the non-matching section  810  in the first sequence is not greater than a predetermined number, the device  100  may determine that an additional matching is to be performed, to find a section, which is similar to the non-matching section  810  in the first sequence, in the second sequence. 
     If the device  100  determines that an additional matching is to be performed on the frames, the device  100  may re-match frames in the first sequence, which are not matched with each other, with frames in the second sequence.  FIG. 8B  shows a process of performing an additional matching on a frame  3  in the first sequence. 
     The device  100  may compare the frame  3  in the first sequence with frames in a range in the second sequence instead of all frames in the second sequence, to match the frame  3  with frames in the second sequence. For example, the device  100  may set a range with reference to a representative matching vector. The range may be a range that includes a frame  5  in the second sequence that is matched with the frame  3  in the first sequence with reference to the representative matching vector, a frame (a frame  4 ) that comes right before the frame  5 , and a frame (a frame  6 ) that comes right after the frame  5 . 
     The device  100  may determine a matching distance between the frame  3  in the first sequence and the frames  4  through  6  in the second sequence, and thus, further match a frame in the second sequence, which has a shortest matching distance from the frame  3  in the first sequence, with the frame  3  in the first sequence. 
     The device  100  may perform an additional matching on the frames  4 ,  5 ,  9 ,  10 , and  13  in the first sequence by performing the same process. 
       FIG. 9  shows completion of a matching between the first sequence and the second sequence according to a process of performing additional matchings and a process of removing redundant matchings. 
     With respect to a frame in the first sequence on which an additional matching is performed, even if the additional matching is removed in a process of removing matchings, the device  100  may not perform an additional matching on the frame. Accordingly, even when the matching is completed, frames that are not matched with each other, such as the frames  5 ,  10 , and  15  in the first sequence, may be present. 
     After the matching is completed, the device  100  may determine a section in the second sequence, which matches a section in the first sequence as a similar section, among sections that include frames in the second sequence. For example, the device  100  may determine a section similar to a first section  910  in the first sequence as a second section  920  in the second sequence. The first section  910  may be a section that includes frames  1  through  4  in the first sequence, and the second section  920  may be a section that includes frames  3  through  6  in the second sequence. 
     Additionally, the device  100  may determine a section similar to a third section  930  in the first sequence as a fourth section  940  in the second sequence. The third section  930  may be a section that includes frames  1  through  14  in the first sequence, and the fourth section  940  may be a section that includes frames  3  through  16  in the second sequence. 
     The device  100  may determine a frame in the second sequence corresponding to a frame in the first sequence and determine similar sections between sequences, by using a matching between frames. 
       FIGS. 10A, 10B, and 10C  are diagrams illustrating a process of pre-processing a sequence to determine a descriptor, which is performed by the device  100 , according to an exemplary embodiment. 
     If the device  100  obtains a plurality of sequences from images different from each other, the device  100  may obtain a sequence of an image to which a border is added, according to a size of a frame. A border may be an upper or lower margin or a boundary margin of a frame for adjusting a size of the frame of an image. For example, a border may be a margin in black or another color. However, a border is not limited thereto, and may include an image. 
     If the device  100  determines a similarity between an original image and an image to which a border is added, the border may be very big noise. Accordingly, the device  100  may remove an area in which the border is located in the obtained sequence before determining a descriptor. Referring to  FIG. 10A through 10C , according to an exemplary embodiment, a process of removing a border area in a first sequence, which is obtained from an image to which a border is added, is shown. 
     Referring to  FIG. 10A , an original frame of a first frame in a first sequence is shown. The original frame of the first frame includes borders  1010  that include upper and lower black margins, and a mark  1020  located on an upper left part of the original frame. The device  100  may select pixels having a same pixel value in a same location in all frames including the first frame that are included in the first sequence. For example, the device  100  may select pixels included in the borders  1010  and pixels included in the mark  102 . Referring to  FIG. 10B , the first frame in which areas of the selected pixels are shown in white (i.e., from which an initial border is removed) is shown. 
     The device  100  may determine a border area by performing calibration to reduce an error in the selection of pixels. In detail, with respect to rows or columns in which the selected pixels are present, if a number of the selected pixels in a row or column is equal to or greater than a predetermined number, the device  100  may determine areas of all pixels in the row or column as border areas. If a number of the selected pixels in a row or column is less than a predetermined number, the device  100  may determine areas of all pixels in the row or column as non-border areas. Referring to  FIG. 10C , the first frame in which the determined border areas are shown in white (i.e., from which a calibrated border is removed) is shown. 
     The device  100  may determine border areas in all frames in the obtained first sequence as being located in same positions in the frames. 
     Additionally, the device  100  may determine a descriptor by using areas other than the determined border areas of the frames in the first sequence. 
     In detail, the device  100  may determine a descriptor of the first frame based on luma information about blocks included in area other than the determined border areas of the first frame in the first sequence. 
     The device  100  may determine border areas in a second sequence by using the same method, and determine a descriptor of a second frame based on luma information about blocks included in areas other than the determined border areas of the second frame in the second sequence. 
       FIG. 11  is a diagram illustrating a process of determining a similarity between sequences, which is performed by the device  100 , according to another exemplary embodiment. 
     According to another exemplary embodiment, the device  100  may determine a similarity between sequences with respect to a plurality of sequences. 
     The device  100  may determine descriptors of sequences different from each other, to determine whether the sequences are similar to each other. A sequence is a part or whole of a sequence, and sequences different from each other may be obtained from an image or images different from each other. Additionally, the sequences different from each other may overlap with each other in an image. 
     The device  100  may determine a descriptor of a frame that represents color information of the frame with respect to frames included in the obtained sequence, and determine a descriptor of the sequence by using the determined descriptor of the frame. 
     For example, the device  100  may determine a descriptor of the first sequence based on a descriptor of a first frame, and determine a descriptor of the second sequence based on a descriptor of a second frame. The device may compare the determined descriptor of the first sequence to the determined descriptor of the second sequence, and thus, determine whether the first sequence is similar to the second sequence. 
     The device  100  may determine the descriptor of the first frame and the descriptor of the second frame, by using the same method described with reference to  FIGS. 4A through 4D . For example, the device  100  may determine the descriptor of the first sequence, based on the descriptor of the first frame that is expressed in a binary code and is information showing a degree of a change in a luma value according to a location in the first frame. 
     In detail, the device  100  may convert the descriptor of the first frame by using a plurality of predetermined codes. For example, the device  100  may select a predetermined code that is most similar to the descriptor of the first frame by comparing the descriptor of the first frame to the plurality of codes, and convert the descriptor into an index number of the selected code. The device  100  may determine a descriptor of the first sequence, based on descriptors of frames in the first sequence including the converted descriptor of the first frame. 
     Additionally, the device  100  may convert descriptors of all frames included in the second sequence, and determine a descriptor of the second sequence based on the converted descriptors of the frames. 
     The device  100  may compare the determined descriptor of the first sequence to the determined descriptor of the second sequence, and determine whether the first sequence is similar to the second sequence based on the comparing. 
       FIGS. 12A, 12B, 13A, and 13B  are diagrams illustrating a process of determining a descriptor of a sequence, which is performed by the device  100 , according to another exemplary embodiment. 
       FIG. 12A  illustrates a process of converting a descriptor  1201  of a first frame in a first sequence, which is expressed in a 20-bit binary code, with reference to a predetermined code. The device  100  may convert the descriptor  1201  of the first frame with reference to a plurality of codes shown in  FIG. 12B . The device  100  may store a plurality of codes that are predetermined by using many training frames. 
     The device  100  may compare the descriptor  1201  of the first frame to the plurality of codes, select a predetermined code that is most similar to the descriptor  1201  of the first frame, and convert the descriptor  1201  of the first frame into an index number of the selected code. 
     For example, the device  100  may calculate a Hamming distance between the descriptor  1201  of the first frame and the plurality of codes, and thus, select a code having a shortest Hamming distance from the descriptor  1201  of the first frame. Referring to  FIGS. 12A and 12B , the device  100  may select a first code  1202  having a Hamming distance of 3 from the descriptor  1201  of the first frame. The device  100  may convert the descriptor  1201  of the first frame into an index number “1” of the selected first code  1202 . 
       FIG. 13A  illustrates a process of converting descriptors of a plurality of frames included in a first sequence. The first sequence may include five frames.  FIG. 13A  shows five descriptors  1301  through  1305  with respect to the five frames. 
     The device  100  may convert a descriptor of each frame into an index number of a code that is most similar to the descriptor, with reference to a plurality of codes. For example, the descriptors  1301 ,  1304 , and  1305  may be converted into an index number “1” of a first code, the descriptor  1302  may be converted into an index number “2” of a second code, and the descriptor  1303  may be converted into an index number “3” of a third code. 
     The device  100  may determine a descriptor of the first sequence based on descriptors of the converted frames. Referring to  FIG. 13B , the device  100  may generate a histogram of descriptors by using the descriptors of the converted frames. 
     The generating of the histogram of the descriptors includes generating of a histogram by arranging the descriptors of the converted frames according to values. In detail, a horizontal axis k of the histogram may consist of the index numbers that are the descriptors of the converted frames. A vertical axis H 1 ( k ) may consist of a number of descriptors of the converted frame in the first sequence that respectively have an index number. 
     For example, the device  100  may generate a histogram shown in  FIG. 13B , by using five converted descriptors  1  through  5  of the frames in the first sequence. The device  100  may determine the histogram, shown in  FIG. 13B , as the descriptor of the first sequence. 
     The device  100  may determine a descriptor of a second sequence by using the same method. 
     The device  100  may determine whether the first sequence is similar to the second sequence, by comparing the descriptor of the first sequence to the descriptor of the second sequence. For example, if a Bhattacharyya distance between the descriptor of the first sequence and the descriptor of the second sequence is less than a predetermined value, the device  100  may determine that the first sequence is similar to the second sequence. A Bhattacharyya distance is a distance for measuring a similarity between two discrete or continuous probability distributions, which, in this case, is calculated to determine a similarity between histograms by using Equation 3 shown below. 
     
       
         
           
             
               
                 
                   
                     
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     In Equation 3, d(Hn,Hm) represents a Bhattacharyya distance between a descriptor Hn of an nth sequence and a descriptor Hm of an mth sequence. 
       FIGS. 14A, 14B, and 14C  are diagrams illustrating a process of determining a descriptor, which is performed by the device  100 , according to another exemplary embodiment. 
     The device  100  may determine a descriptor of a frame by using color information, with respect to frames included in an obtained sequence. For example, a descriptor of a frame may be a histogram generated by using a distribution of intensity information about colors for each channel in a frame represented in a red, green, and blue color (RGB) space. If the descriptor of the frame is determined by using intensity information about colors in the RGB color space, the device  100  may determine a descriptor that is robust to a change such as cropping, rotation, or symmetry movement of a sequence. 
     Referring to  FIG. 14A , the device  100  may divide a first frame in a first sequence into a channel R, a channel G, and a channel B in an RGB color space. The device  100  may generate a histogram with respect to a pixel value of pixels (i.e., intensity information) included in a frame for each channel, and thus, generate a histogram for the channel R, a histogram for the channel G, and a histogram for the channel B.  FIG. 14B  illustrates a histogram for each channel. Referring to  FIG. 14B , histograms for respective channels are shown. 
       FIG. 14C  shows a descriptor of the first frame. The device  100  may combine the generated histograms for the respective channels with each other, and normalize the histograms. The device  100  may determine a histogram, shown in  FIG. 14C , as the descriptor of the first frame. 
     The device  100  may convert the descriptor of the first frame into an index number of a predetermined histogram, with reference to a plurality of histograms instead of a plurality of codes. The device  100  may determine the descriptor of the first sequence based on the converted descriptor of the first frame and descriptors of frames included in the first sequence. 
       FIGS. 15A, 15B, and 15C  are diagrams illustrating a process of determining a descriptor, which is performed by the device  100 , according to another exemplary embodiment. 
     The device  100  may determine a descriptor of a frame by using color information, with respect to frames included in an obtained sequence. For example, a descriptor of a frame may be histogram information generated by using a distribution of hue information in the frame expressed in a hue, saturation, and value (HSV) color space. If a descriptor of a frame is determined by using hue information about the HSV color space, the device  100  may determine a descriptor that is robust to a chrome change and an intensity change in a whole sequence. 
     Referring to  FIG. 15A , the device  100  may convert a first frame represented in an RGB color space into a frame represented in a HSV color space. The device  100  may generate a histogram by using hue information about pixels included in the converted first frame for a channel H. The device  100  may generate a histogram by quantizing hue information about each pixel into predetermined representative colors. 
     Additionally, referring to  FIG. 15B , the device  100  may divide a first frame into a plurality of blocks, generate histograms by using the quantized color information about pixels included in respective blocks, and then, generate a histogram for the first frame by combining the histograms for the respective blocks with each other. 
       FIG. 15C  illustrates the histogram obtained by combining the histograms with each other for the first frame. The device  100  may determine the histogram, shown in  FIG. 15C , as a descriptor of the first frame. 
     The device  100  may convert the descriptor of the first frame into an index number of a predetermined histogram, with reference to a plurality of histograms instead of a plurality of codes. The device  100  may determine a descriptor of the first sequence based on the converted descriptor of the first frame and descriptors of frames included in the first sequence. 
     The device  100  may determine three descriptors of a frame, by using a method using luma information about a frame, a method using intensity information about an RGB color space, and a method using hue information about a HSV color space. The device  100  may determine a plurality of descriptors of a sequence, based on the three descriptors of the frame that are determined by using the three methods different from each other. 
     For example, the device  100  may determine a first descriptor of the first sequence based on the method using luma information, determine a second descriptor of the first sequence based on the method using a pixel value for each channel in an RGB color space, and determine a third descriptor by using a method using hue information about a HSV color space. Then, the device  100  may accurately determine a similarity between sequences by comparing descriptors of other sequences to the first descriptor, the second descriptor, and the third descriptors at a same time. 
     If a distance between any of the first through third descriptors of the first sequence and descriptors of the second sequence is less than a predetermined value, the device  100  may determine that the first sequence is similar to the second sequence. 
     The device  100  may provide a similarity determination method robust to a variation of a sequence, by using descriptors determined by using the methods different from each other. 
       FIG. 16  is a flowchart of a method of determining a similarity between sequences, which is performed by the device  100 , according to an exemplary embodiment. 
     Referring to  FIG. 16 , in operation  1610 , the device  100  obtains a first sequence and a second sequence that each includes a plurality of frames. The device  100  may obtain a first sequence and a second sequence from an image or images different from each other that are stored in the device  100  or an external apparatus. 
     The device  100  may extract one or more frames included in the first sequence as one or more key frames in the first sequence according to a predetermined frame rate, and extract one or more frames included in the second sequence as one or more key frames in the second sequence according to the frame rate. 
     Additionally, the device  100  may determine a border area of the obtained sequence, and thus, perform pre-processing for removing the border area. 
     In operation  1602 , the device  100  determines a descriptor of a first frame included in the first sequence based on luma information about a plurality of blocks in the first frame included in the first sequence. The first frame may be one of the key frames in the first sequence. 
     In detail, the device  100  may divide the first frame into a plurality of blocks that have a same size and include a plurality of pixels. The device  100  may determine luma information about respective blocks by using a luma value of pixels included in the respective blocks. For example, luma information about a block may be an average luma value of the block. 
     The device  100  may compare the determined luma information about the respective blocks to each other, and thus, determine information about a luma difference between the blocks. The information about the luma difference between blocks may be information obtained by numeralizing a value of a difference between average luma values of the respective blocks according to a reference section. For example, the information about the luma difference may be determined as a 2-bit binary code according to a value of a difference between average luma values of two blocks. The device  100  may determine a descriptor of the first frame that is expressed in a 2-bit binary code based on information about a luma difference between the plurality of blocks. 
     In operation  1603 , the device  100  determines a descriptor of a second frame included in the second sequence based on luma information about a plurality of blocks in the second frame included in the second sequence. The second frame may be one of key frames in the second sequence. The device  100  may determine a 2-bit binary code, which includes the luma information about the plurality of blocks in the second frame, as a descriptor of the second frame. 
     In operation  1604 , the device  100  determines a similarity between the first sequence and the second sequence by using the descriptor of the first frame and the descriptor of the second frame. 
     In detail, the device  100  may match the first frame with the second frame by using the descriptor of the first frame and the descriptor of the second frame. The device  100  may determine frames having a shortest matching distance from each other as similar frames, and match the frames with each other. For example, the device  100  may calculate a Hamming distance between descriptors of frames, and thus, determine a matching distance between the frames. 
     The device  100  may calculate a Hamming distance between the descriptor of the first frame in the first sequence and descriptors of all frames in the second sequence and match a frame in the second sequence, which has a shortest Hamming distance from the descriptor of the first frame, with the first frame. For example, if a Hamming distance between the descriptor of the first frame and the descriptor of the second frame is shortest, the device  100  may match the first frame with the second frame. 
     The device  100  may determine a section in the second sequence, which is similar to, and thus, matches a section in the first sequence, based on a matching relation between the frames that is obtained by using the descriptors. 
     Additionally, the device  100  may represent a matching relation between frames, which is obtained by using descriptors, as a matching vector. The device  100  may determine a representative matching vector by using the matching vector, to speed up the determining of similarity. For example, the device  100  may determine a matching vector having a highest frequency, among matching vectors between sequences different from each other, as a representative matching vector. 
     The device  100  may determine matchings having a matching vector that is not similar to the representative matching vector, among matching, as noise by using the determined representative matching vector, and thus, remove the matchings. The device  100  may perform additional matching on frames whose matching was removed. In detail, the device  100  may perform the additional matching on frames whose matching was removed so that a matching vector of the additional matching is similar to the representative matching vector. For example, the similarity determiner  130  may match the frames with each other within a range according to the representative matching vector. 
     The device  100  may determine a section in the second sequence that matches a section in the first sequence as a section similar to the section in the first sequence, based on matching vectors of the additional matching. 
       FIG. 17  is a flowchart of a method of determining a similarity between sequences, which is performed by the device  100 , according to another exemplary embodiment. 
     Referring to  FIG. 17 , in operation  1701 , the device  100  obtains a first sequence and a second sequence. 
     In operations  1702  and  1703 , the device  100  determines a descriptor of a first frame included in the first sequence based on luma information about a plurality of blocks in the first frame included in the first sequence, and determines a descriptor of a second frame included in the second sequence based on luma information about a plurality of blocks in the second frame included in the second sequence. 
     Additionally, the device  100  may determine a descriptor of the first frame by using a pixel value for each channel with respect to the first frame in the first sequence represented in an RGB color space. 
     The device  100  may divide the first frame in the first sequence into the channel R, the channel G, and the channel B in the RGB color space. The device  100  may generate a histogram by using pixel values of pixels included in the first frame for each channel, and thus, generate a histogram for the channel R, a histogram for the channel G, and a histogram for the channel B. The device  100  may combine the generated histograms for respective channels with each other, and normalize the histograms, and thus, determine the descriptor of the first frame. 
     The device  100  may determine a descriptor of the second frame included in the second sequence, by using pixel values for each channel in the RGB color space in the same method. 
     Additionally, the device  100  may determine a descriptor of the first frame by using color information in a channel H in the HSV color space with respect to the first frame in the first sequence. 
     The device  100  may generate a histogram by quantizing the hue information in the channel H in the HSV color space as a predetermined number of a representative color, and thus, determine the descriptor of the first frame. Alternately, the device  100  may divide the first frame into a plurality of blocks, and thus, generate histograms for hue information about pixels included in each block. Then, the device  100  may determine a histogram for the first frame by combining the generated histograms for the respective blocks with each other. 
     The device  100  may determine a descriptor of the second frame in the second sequence by employing the same method using color information in the channel H in the HSV color space. 
     In operation  1704 , the device  100  determines a descriptor of the first sequence based on the descriptor of the first frame, and determines a descriptor of the second sequence based on the descriptor of the second frame. 
     If the descriptor of the first frame is a binary code, the device  100  may convert the descriptor of the first frame with reference to a plurality of predetermined codes. The device  100  may select a predetermined code that is most similar to the descriptor of the first frame, from the plurality of codes, and convert the descriptor of the first frame into an index number of the selected code. The device  100  may convert the descriptor of the second frame by using the same method. 
     Additionally, if the descriptor of the first frame is a histogram, the device  100  may convert the descriptor of the first frame with reference to a plurality of predetermined histograms. The device  100  may select a predetermined histogram that is most similar to the descriptor of the first frame, from the plurality of histograms, and convert the descriptor of the first frame into an index number of the selected histogram. The device  100  may convert the descriptor of the second frame by using the same method. 
     The device  100  may determine the descriptor of the first sequence based on the converted descriptors of all frames in the first sequence including the first frame. The device  100  may generate a histogram by using the converted descriptor of all the frames, and determine the generated histogram as a descriptor of the first sequence. The device  100  may determine a descriptor of the second sequence based on converted descriptors of all frames in the second sequence including a second frame, by using the same method. 
     The device  100  may determine three descriptors of a frame, by using a method using luma information about the frame, a method using intensity information in an RGB color space, and a method using hue information about a HSV color space. The device  100  may determine a plurality of descriptors of a sequence, based on the three descriptors of the frame that are determined by using the three methods different from each other. 
     For example, the device  100  may determine a first descriptor of the first sequence based on the method using luma information, determine a second descriptor of the first sequence based on the method using a pixel value for each channel in an RGB color space, and determine a third descriptor based on using a method using hue information about a HSV color space. 
     In operation  1705 , the device  100  determines a similarity between the first sequence and the second sequence by using the descriptor of the first sequence and the descriptor of the second sequence. For example, the device  100  may determine whether the first sequence is similar to the second sequence, by comparing the descriptor of the first sequence to the descriptor of the second sequence. 
     For example, if a Bhattacharyya distance between the descriptor of the first sequence and the descriptor of the second sequence is less than a predetermined value, the device  100  may determine that the first sequence is similar to the second sequence. 
     Additionally, if a plurality of descriptors is determined with respect to the first sequence and the second sequence by using methods different from each other, the device  100  may determine whether the first sequence is similar to the second sequence by using the plurality of descriptors. For example, if a first descriptor, a second descriptor, and a third descriptors of the first sequence are determined, the device  100  may compare the first through third descriptors of the first sequence respectively to descriptors of the second sequence that are determined by using the same method. 
     The device  100  may determine a similarity between the first sequence and the second sequence by combining results obtained by comparing respective descriptors with each other that are obtained by using a plurality of methods. For example, if the device  100  determines that the descriptor of the first sequence and the descriptor of the second sequence obtained by using a method, among descriptors obtained by using the plurality of methods, are similar to each other, the device  100  may determine that the first sequence is similar to the second sequence. 
     The device  100  may provide a similarity determination method robust to a variation of a sequence, by using descriptors determined by using the methods different from each other. 
     According to one or more exemplary embodiments, the device for determining a similarity between sequences in an image may quickly and accurately determine a similarity between moving pictures, similar to each other, and provide information about a similarity between similar moving images, similar to each other, to a user. 
     It will be understood that the terms “comprises” and/or “comprising” used herein specify the presence of stated features or components, but do not preclude the presence or addition of one or more other features or components. In addition, the terms such as “unit,” “-er (-or),” and “module” described in the specification refer to an element for performing at least one function or operation, and may be implemented in hardware, software, or the combination of hardware and software. 
     The computer-readable code may be recorded and/or transferred on a medium in a variety of ways, and examples of the medium include recording media, such as magnetic storage media (e.g., ROM, floppy disks, hard disks, etc.) and optical recording media (e.g., compact disc read only memories (CD-ROMs) or digital versatile discs (DVDs)), and transmission media such as Internet transmission media. Thus, the medium may have a structure suitable for storing or carrying a signal or information, such as a device carrying a bitstream according to one or more exemplary embodiments. The medium may also be on a distributed network, so that the computer-readable code is stored and/or transferred on the medium and executed in a distributed fashion. Furthermore, the processing element may include a processor or a computer processor, and the processing element may be distributed and/or included in a single device. 
     The foregoing exemplary embodiments are examples and are not to be construed as limiting. The present teaching can be readily applied to other types of apparatuses. Also, the description of the exemplary embodiments is intended to be illustrative, and not to limit the scope of the claims, and many alternatives, modifications, and variations will be apparent to those skilled in the art.