METHOD AND APPARATUS FOR CLASSIFYING SAMPLES

1 . A computer-implemented method for classifying samples, comprising:

obtaining a candidate sample (T) and respective features (Ft) of the candidate sample (T);

selecting N samples from a classification sample library, where N is a predetermined positive integer;

determining a feature similarity (SIMi) between the candidate sample (T) and each of the N samples (i), wherein the feature similarity (SIMi) is determined based on the respective features (Ft) of the candidate sample (T) and respective features (Fi) of each sample (i);

obtaining a sample quality (Qi), wherein the sample quality (Qi) is of each sample (i);

determining, as comprehensive similarity measures (Si), a comprehensive similarity measure (Si) between the candidate sample (T) and each sample (i) at least based on a difference (ri) between the feature similarity (SIMi) and the sample quality (Qi); and

determining, based on the comprehensive similarity measures (Si), whether the candidate sample (T) belongs to a classification within the classification sample library.

2 . The computer-implemented method according to claim 1 , wherein the selecting the N samples from the classification sample library comprises:

determining a feature similarity between the candidate sample (T) and each of M samples based on the respective features (Ft) of the candidate sample (T) and respective features of each of the M samples in the classification sample library, where M is a predetermined positive integer and is greater than N; and

selecting the N samples from the M samples based on the feature similarity between the candidate sample (T) and each of the M samples.

3 . The computer-implemented method according to claim 2 , wherein selecting the N samples from the M samples based on the feature similarity between the candidate sample (T) and each of the M samples, comprises:

selecting N samples from the M samples, wherein, in relation to the candidate sample (T), the feature similarity (SIMi) of the N samples are highest in value.

4 . The computer-implemented method according to claim 1 , wherein selecting the N samples from the classification sample library comprises:

sorting samples in the classification sample library according to respective sample qualities of the samples.

5 . The computer-implemented method according to claim 1 , wherein the feature similarity (SIMi) is determined by normalizing a distance between the respective features (Ft) of the candidate sample (T) and the respective features (Fi) of each sample (i).

6 . The computer-implemented method according to claim 1 , wherein determining a comprehensive similarity measure (Si) between the candidate sample (T) and each sample (i) comprises:

determining the comprehensive similarity measure (Si) as Si=a+b*ri*c, wherein a+b=1 and c is a coefficient associated with the sample quality (Qi).

7 . The computer-implemented method according to claim 6 , wherein:

if ri>=0: c=1/(1−Qi); and

if ri<0: c=1/Qi.

8 . The computer-implemented method according to claim 1 , wherein determining, based on the comprehensive similarity measures (Si), whether the candidate sample (T) belongs to a classification within the classification sample library, comprises:

determining, based on the comprehensive similarity measures (Si), a combined similarity score of the candidate sample; and

determining, based on the combined similarity score, whether the candidate sample (T) belongs to the classification within the classification sample library.

9 . The computer-implemented method according to claim 8 , wherein determining, based on the comprehensive similarity measures (Si), a combined similarity score of the candidate sample, comprises:

if at least one ri is greater than or equal to 0, determining the combined similarity score using a maximum value among the comprehensive similarity measures (Si); or

if no ri is greater than or equal to 0, determining the combined similarity score using a minimum value among the comprehensive similarity measures (Si).

10 . The computer-implemented method according to claim 8 , wherein determining the combined similarity score of the candidate sample comprises:

determining the combined similarity score using an average value of the comprehensive similarity measures (Si) between the candidate sample (T) and the N samples (i).

11 . A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations for classifying samples, comprising:

obtaining a candidate sample (T) and respective features (Ft) of the candidate sample (T);

selecting N samples from a classification sample library, where N is a predetermined positive integer;

determining a feature similarity (SIMi) between the candidate sample (T) and each of the N samples (i), wherein the feature similarity (SIMi) is determined based on the respective features (Ft) of the candidate sample (T) and respective features (Fi) of each sample (i);

obtaining a sample quality (Qi), wherein the sample quality (Qi) is of each sample (i);

determining, as comprehensive similarity measures (Si), a comprehensive similarity measure (Si) between the candidate sample (T) and each sample (i) at least based on a difference (ri) between the feature similarity (SIMi) and the sample quality (Qi); and

determining, based on the comprehensive similarity measures (Si), whether the candidate sample (T) belongs to a classification within the classification sample library.

12 . The non-transitory, computer-readable medium according to claim 11 , wherein the selecting the N samples from the classification sample library comprises:

determining a feature similarity between the candidate sample (T) and each of M samples based on the respective features (Ft) of the candidate sample (T) and respective features of each of the M samples in the classification sample library, where M is a predetermined positive integer and is greater than N; and

selecting the N samples from the M samples based on the feature similarity between the candidate sample (T) and each of the M samples.

13 . The non-transitory, computer-readable medium according to claim 12 , wherein selecting the N samples from the M samples, comprises:

selecting N samples from the M samples, wherein, in relation to the candidate sample (T), the feature similarity (SIMi) of the N samples are highest in value.

14 . The non-transitory, computer-readable medium according to claim 11 , wherein selecting the N samples from the classification sample library comprises:

sorting samples in the classification sample library according to respective sample qualities of the samples.

15 . The non-transitory, computer-readable medium according to claim 11 , wherein the feature similarity (SIMi) is determined by normalizing a distance between the respective features (Ft) of the candidate sample (T) and the respective features (Fi) of each sample (i).

16 . The non-transitory, computer-readable medium according to claim 11 , wherein determining a comprehensive similarity measure (Si) between the candidate sample (T) and each sample (i) comprises:

determining the comprehensive similarity measure (Si) as Si=a+b*ri*c, wherein a+b=1 and c is a coefficient associated with the sample quality (Qi).

17 . The non-transitory, computer-readable medium according to claim 16 , wherein:

if ri>=0: c=1/(1−Qi); and

if ri<0: c=1/Qi.

18 . The non-transitory, computer-readable medium according to claim 11 , wherein determining, based on the comprehensive similarity measures (Si), whether the candidate sample (T) belongs to a classification within the classification sample library, comprises:

determining, based on the comprehensive similarity measures (Si), a combined similarity score of the candidate sample; and

determining, based on the combined similarity score, whether the candidate sample (T) belongs to the classification within the classification sample library.

19 . The non-transitory, computer-readable medium according to claim 18 , wherein determining a combined similarity score of the candidate sample, comprises:

if at least one ri is greater than or equal to 0, determining the combined similarity score using a maximum value among the comprehensive similarity measures (Si); or

if no ri is greater than or equal to 0, determining the combined similarity score using a minimum value among the comprehensive similarity measures (Si).

20 . The non-transitory, computer-readable medium according to claim 18 , wherein determining the combined similarity score of the candidate sample comprises:

determining the combined similarity score using an average value of the comprehensive similarity measures (Si) between the candidate sample (T) and the N samples (i).

21 . A computer-implemented system for classifying samples, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:

obtaining a candidate sample (T) and respective features (Ft) of the candidate sample (T);

selecting N samples from a classification sample library, where N is a predetermined positive integer;

determining a feature similarity (SIMi) between the candidate sample (T) and each of the N samples (i), wherein the feature similarity (SIMi) is determined based on the respective features (Ft) of the candidate sample (T) and respective features (Fi) of each sample (i);

obtaining a sample quality (Qi), wherein the sample quality (Qi) is of each sample (i);

determining, as comprehensive similarity measures (Si), a comprehensive similarity measure (Si) between the candidate sample (T) and each sample (i) at least based on a difference (ri) between the feature similarity (SIMi) and the sample quality (Qi); and

determining, based on the comprehensive similarity measures (Si), whether the candidate sample (T) belongs to a classification within the classification sample library.

22 . The computer-implemented system according to claim 21 , wherein the selecting the N samples from the classification sample library comprises:

determining a feature similarity between the candidate sample (T) and each of M samples based on the respective features (Ft) of the candidate sample (T) and respective features of each of the M samples in the classification sample library, where M is a predetermined positive integer and is greater than N; and

selecting the N samples from the M samples based on the feature similarity between the candidate sample (T) and each of the M samples.

23 . The computer-implemented system according to claim 22 , wherein the selecting the N samples from the M samples comprises:

selecting, from the M samples, the N samples with highest feature similarities with the candidate sample (T).

24 . The computer-implemented system according to claim 21 , wherein selecting the N samples from the classification sample library comprises:

sorting samples in the classification sample library according to respective sample qualities of the samples.

25 . The computer-implemented system according to claim 21 , wherein the feature similarity (SIMi) is determined by normalizing a distance between the respective features (Ft) of the candidate sample (T) and the respective features (Fi) of each sample (i).

26 . The computer-implemented system according to claim 21 , wherein determining a comprehensive similarity measure (Si) between the candidate sample (T) and each sample (i) comprises:

determining the comprehensive similarity measure (Si) as Si=a+b*ri*c, wherein a+b=1 and c is a coefficient associated with the sample quality (Qi).

27 . The computer-implemented system according to claim 26 , wherein:

if ri>=0: c=1/(1−Qi); and

if ri<0: c=1/Qi.

28 . The computer-implemented system according to claim 21 , wherein determining, based on the comprehensive similarity measures (Si), whether the candidate sample (T) belongs to a classification within the classification sample library, comprises:

determining, based on the comprehensive similarity measures (Si), a combined similarity score of the candidate sample; and

determining, based on the combined similarity score, whether the candidate sample (T) belongs to the classification within the classification sample library.

29 . The computer-implemented system according to claim 28 , wherein determining a combined similarity score of the candidate sample, comprises:

if at least one ri is greater than or equal to 0, determining the combined similarity score using a maximum value among the comprehensive similarity measures (Si); or

if no ri is greater than or equal to 0, determining the combined similarity score using a minimum value among the comprehensive similarity measures (Si).

30 . The computer-implemented system according to claim 28 , wherein determining the combined similarity score of the candidate sample comprises:

determining the combined similarity score using an average value of the comprehensive similarity measures (Si) between the candidate sample (T) and the N samples (i).