Office Action 2: Final Rejection (2024-07-11)
Rejection types: 102 - Lack of Novelty

CLAIMS:
IN THE CLAIMS:

Please amend the claims, as set forth below:

1\. (Currently amended) An image analysis method, comprising:
 receiving a target image for processing by an image analysis model composed of at least one expert generated by learning a training image;
analyzing the target image based on an output value from the at least one expert to generate an analysis result;
determining validity of \[\[an\]\]the analysis result for the target image based on a probability of an input target image belonging to an input category of the at least one expert; and
 responsive to determining that the analysis result for the target image is not valid, classifying the target image into a new category so that a new expert for the new category is trained using the target image.

2\. (Currently amended) The method of claim 1, wherein the determining of the validity of the analysis result for the target image comprises randomly determining the validity of the analysis result for the target image based on a probability of the input target image belongingto the input category of the at least one expert.

3\. (Currently amended) The method of claim 1, wherein the determining of the validity of the analysis result for the target image comprises:
obtaining a likelihood of an input and a class with respect to each of the at least one expert;
obtaining a prior probability of each of the at least one expert; and
obtaining a probability of the input target image belongingto the input category of the at least one expert based on the obtained likelihood and the obtained prior probability.

4\. (Original) The method of claim 3, wherein the obtaining the likelihood of an input and a class with respect to each of the at least one expert comprises:
obtaining a first probability of a class with respect to each of the input target image and the at least one expert by using a discriminative model of each of the at least one expert;
obtaining a second probability of an input with respect to each of the at least one expert by using a generative model of each of the at least one expert; and
obtaining the likelihood of an input and a class with respect to each of the at least one expert based on the first probability and the second probability.

5\. (Original) The method of claim 4, wherein the obtaining the second probability comprises estimating a lower bound of the second probability as the second probability.

6\. (Currently amended) The method of claim 1, further comprising:
storing the target image classified into the new category in a memory; and
when an accumulated capacity of images including the target image and stored in the memory is greater than or equal to a predetermined reference capacity, generating the new expert for the new category by performing training using the images stored in the memory.

7\. (Currently amended) The method of claim 6, wherein the generating of the expert for the new category comprises:
labeling the images stored in the memory; and
generating the new expert for the new category by training the new expert using the labeled images.

8\. (Currently amended) An image analysis apparatus, comprising:
one or more processors;
a memory storing instructions thereon, the instructions when executed by the one or more processors cause the one or more processors to: analyze a target image based on an image analysis model composed of at least one expert generated by learning training images to generate an analysis result;
 determine validity of \[\[an\]\]the analysis result for the target image based on a probability of the target image belongingto an input category of the at least one expert\[\[,\]\]; and
responsive to determining that the analysis result for the target image is invalid, classify the target image into a new category so that a new expert for the new category is trained using the target image.

9\. (Currently amended) The apparatus of claim 8, further comprising: wherein the memory is further configured to store the target image classified into the new category.

10\. (Currently amended) The apparatus of claim 9, wherein the instructions further cause the one or more processors to:
 when an accumulated capacity of images including the targe image stored in the memory is greater than or equal to a predetermined reference capacity, generate the new expert for the new category by performing training using the images stored in the memory.

11\. (Currently amended) The apparatus of claim 10, wherein the instructions further cause the one or more processors to:
 label the images stored in the memory, and
 generate the new expert for the new category by training the new expert using the labeled images.

12\. (Currently amended) The apparatus of claim 8, wherein the instructions further cause the one or more processors to randomly determine the validity of the analysis result for the target image based on a probability of the input target image belongingto the input category of the at least one expert.

13\. (Currently amended) The apparatus of claim 8, wherein the instructions further cause the one or more processors to:
obtain a likelihood of an input and a class with respect to each of the at least one expert,
obtain a prior probability of each of the at least one expert, and
obtain a probability of the input target image belongingto the input category of the at least one expert based on the obtained likelihood and the obtained prior probability.

14\. (Currently amended) The apparatus of claim 13, wherein the instructions further cause the one or more processors to:
obtain a first probability of a class with respect to each of the input target image and the at least one expert by using a discriminative model of each of the at least one expert,
obtain a second probability of an input with respect to each of the at least one expert by using a generative model of each of the at least one expert, and
obtain the likelihood of an input and a class with respect to each of the at least one expert based on the first probability and the second probability.

15\. (Currently amended) The apparatus of claim 14, wherein the instructions further cause the one or more processors to estimate a lower bound of the second probability as the second probability.

16\. (Original) A method for generating an image analysis model, the method comprising:
continually receiving first training images during a first receiving period;
generating a first expert composed of a discriminative model and a generative model for a first category by learning the first training images during a first learning period right after the first receiving period;
continually receiving second training images during a second receiving period right after the first learning period;
determining whether to learn each of the second training images for the first expert based on a probability of each of the second training images to belong to the first category; and
generating a second expert for a second category by learning, during a second learning period right after the second receiving period, a training image determined not to be learned for the first expert among the second training images.

OFFICE ACTION:
DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment and written response filed 03/21/2024 have been entered and considered.
Claims 1-3 and 6-15 have been amended.
Claims 1-16 are pending.
Response to Arguments
Applicant’s arguments with respect to independent claims 1 and 8 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Upon entry of the claim amendment, the rejection of claims 8-15 under 35 U.S.C. 112(f) has been withdrawn.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2 and 6-12 are are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhatt et al (“Bhatt” hereinafter, U.S. Publication No. 2019/0220967A1).
As per claim 1, Bhatt discloses an image analysis method (abstract: ) comprising: receiving a target image for processing by an image analysis model (paragraph [0041]: step 302: “receive … a plurality of images”) composed of at least one expert generated by learning a training image (paragraph [0044]: step 306: “forward passing the received image through a neural network model pre-trained on a database of a plurality of images pertaining to a varied contexts”); analyzing the target image based on an output value from the at least one expert to generate an analysis result (paragraph [0053]: “the pre-trained tagger models 130a facilitate classifying of the one or more classes identified in the first level of classification into the one or more sub-classes at step 312b”); determining validity of the analysis result for the target image based on a probability of an input target image belonging to an input category of the at least one expert (paragraph [0053]: “a confidence level may be computed for the classified one or more sub-classes, at step 312c, based on which the pre-trained tagger models 130a may be re-trained”); and responsive to determining that the analysis result for the target image is not valid (paragraph [0054]: “the classification module 130f may be further configured to evaluate the received images associated with the confidence level below the pre-defined threshold with the obtained metadata prior to re-training the pre-trained tagger models 130a at step 312d to ensure a valid reason for re-training based on expert tagging”), classifying the target image into a new category so that a new expert for the new category is trained using the target image (paragraph [0049]: “The step 308 of clustering may also flag a new class in the absence of similarity between the extracted features of the received images and the features associated with the one or more classes identified at step 308b-1”; paragraph [0053]: “The re-trained tagger models represent the classification models 130d. The re-training and associated self-learning makes the system 200 intelligent and adaptable to computer vision requirements with minimal human intervention that occurs only in the initial iterations”; paragraph [0116]: “as the cluster wise classifiers are independent of each other, if a new class has to be introduced, only a classifier of the images belonging to sub-classes of that cluster needs to be trained, instead of re-training the whole classification system”).
As per claim 2, Bhatt discloses wherein the determining of the validity of the analysis result for the target image comprises randomly determining the validity of the analysis result for the target image based on a probability of the input image belonging to the input category of the at least one expert (paragraph [0054]: “the classification module 130f may be further configured to evaluate the received images associated with the confidence level below the pre-defined threshold with the obtained metadata prior to re-training the pre-trained tagger models 130a at step 312d to ensure a valid reason for re-training based on expert tagging”).
As per claim 6, Bhatt discloses storing the target image classified into the new category in a memory; and wherein an accumulated capacity of image including the target image and stored in the memory is greater than or equal to a predetermined reference capacity, generating the new expert for the new category by performing training using the image stored in the memory (as explained above, the received images are stored in a memory and the confidence level of the images are below a threshold are used to retrain a classifier as a new class. The examiner notes the claimed “predetermined reference capacity” is inherently to be more than 1 image).
As per claim 7, Bhatt discloses wherein the generating of the expert for the new category comprises: labeling the image stored in the memory; and generating the new expert for the new category by training the new expert using the labeled images (paragraph [0053]: “The re-trained tagger models represent the classification models 130d. The re-training and associated self-learning makes the system 200 intelligent and adaptable to computer vision requirements with minimal human intervention that occurs only in the initial iterations”. Although it is minimal human intervention, some of the images are trained through human expert labeling).
As per claim 8, see explanation in claim 1. Bhatt’s system is a computer system, which inherently includes a processor and a memory.
As per claim 9, see explanation in claim 2.
As per claim 10, see explanation in claim 2.
As per claim 11, see explanation in claim 7.
As per claim 12, Bhatt teaches the received images are randomly subject to image analysis classification, and the images with confidence level below a threshold are used to retrain a classifier.
Allowable Subject Matter
Claims 3-5 and 13-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 16 is allowed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

APPLICANT REBUTTAL: REMARKS
Claims 1-16 were pending in this application. In the Office Action dated January 11,
2024, claims 1-3, 8-9 and 12-13 were rejected, claims 4-7, 10-11 and 14-15 were objected to, and claim 16 was allowed.
Claims 1-3 and 6-15 are hereby amended. No claim is added or canceled herein.
Based on the above Amendment and the following Remarks, withdrawal of all outstanding objections and rejections is respectfully requested.
Res
p
onse to Claim Inter
p
retation under 35 U.S.C.
112(f
)
In the Office Action, certain terms in claims 8-15 were interpreted under 35 U.S.C.
§ 112(f). Specifically, claim limitations such as "image analysis unit," determination unit," and "learning unit" in these claims were interpreted under 35 U.S.C. § 112(f).
Applicant respectfully disagrees with these claims are to be interpreted under 35 U.S.C. § 112(f) because these claim limitations do not recite the term "means" and the language connote sufficient structures. However, to clarify that the interpretation under 35 U.S.C. § 112(f) is not invoked, the claims are hereby amended to remove the term "unit."
Accordingly, Applicant respectfully submits that claims 8-15, as amended, do not recite any claim elements to be interpreted under 35 U.S.C. § 112(f).
Response to Re
j
ections under 35 U.S.C.
&
In the Office Action, claims 1-3, 8-9 and 12-13 were rejected under 35 U.S.C. § 102(a)(2) as being anticipated by U.S. Patent Application Publication No. 2015/0332157 ("Baughman").
Independent claim 1, as amended, recites the feature of "responsive to determining that the analysis result for the target image is not valid, classifying the target image into a new category so that a new expert for the new category is trained using the target image...."
Baughman fails to disclose this feature. Baughman at best discloses producing a report that includes the top N candidate hypotheses based on the determined probabilities and/or confidence values of the hypotheses (see Baughman, paragraph [0027]). That is, in Baughman, the number "N" indicating the top number of candidate hypotheses may be set by a user and remains unchanged regardless of the probabilities and/or confidence values of the top candidate hypotheses (see Id.). Hence, in Baughman, a new category of a new hypotheses is not established nor images classified into such new category when the top probabilities and/or confidence values are low. Accordingly, Baughman fails to disclose this feature of amended claim 1.
Hence, amended claim 1 and its dependent claims are patentably distinguishable over Baughman.
Independent claim 8, as amended, also a similar feature. Hence, amended claim 8 and its dependent claims are also patentably distinguishable over Baughman.
Therefore, withdrawal of the rejection is respectfully requested.
Allowability of Claims Is Acknow
ledged
Allowability of claims 4-7, 10-11, and 14-16 is respectfully acknowledged.
Conclusion

NEXT OFFICE ACTION: DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment and written response filed 09/11/2024 have been entered and considered.
Claims 3 and 13 have been cancelled.
Claims 1, 4, 8 and 14 have been amended.
Claims 1-2, 4-12 and 14-16 are pending.
Upon entry of the claim amendment, the rejection and objection of claims 1-2, 4-12 and 14-15 have been withdrawn.
Allowable Subject Matter
Claims 1-2, 4-12 and 14-16 are allowed.
The following is an examiner’s statement of reasons for allowance:
Independent claims 1 and 8 have been amended to incorporate allowable subject matter as previously indicated in office action dated 07/11/2024.
Claims 2 and 4-7 are dependent upon claim 1.
Claims 9-12 and 14 are dependent upon claim 8.
Claim 16 defines features of “generating a first expert composed of a discriminative model and a generative model for a first category by learning the first training images during a first learning period right after the first receiving period; continually receiving second training images during a second receiving period right after the first learning period; determining whether to learn each of the second training images for the first expert based on a probability of each of the second training images to belong to the first category; and generating a second expert for a second category by learning, during a second learning period right after the second receiving period, a training image determined not to be learned for the first expert among the second training images”, which are not taught or suggested by the art of record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US20190220967A1: Claims:
A processor implemented method ( 300 ) comprising: receiving a plurality of images, at one or more time intervals, pertaining to a context under consideration, the received images being correlated and associated with at least one of a spatial and temporal information ( 302 ); identifying and transmitting, at the one or more time intervals, at least a subset of the received images based on the spatial or temporal information and an adaptive threshold ( 304 ); extracting features from the received images, by forward passing the received images through a neural network model pre-trained on a dataset of a plurality of images pertaining to varied contexts ( 306 ); performing a first level of inferencing ( 308 ), by clustering the received images into one or more classes using the extracted features, the step of clustering comprising: determining an optimal number of the one or more classes using a Silhouette coefficient ( 308 a ); identifying the one or more classes based on similarity detected between the received images ( 308 b ) by performing at least one of: computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images ( 308 b - 1 ); computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP) ( 308 b - 2 ); and computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes ( 308 b - 3 ); wherein the number of the one or more classes equals the determined optimal number; and validating the quality of the one or more classes using one or more of an Normalized Mutual Information (NMI) score, a Rand Index and a purity measure ( 308 c ); and associating the one or more classes with a tag based on the context under consideration ( 310 ).
receiving a plurality of images, at one or more time intervals, pertaining to a context under consideration, the received images being correlated and associated with at least one of a spatial and temporal information ( 302 );
identifying and transmitting, at the one or more time intervals, at least a subset of the received images based on the spatial or temporal information and an adaptive threshold ( 304 );
extracting features from the received images, by forward passing the received images through a neural network model pre-trained on a dataset of a plurality of images pertaining to varied contexts ( 306 );
performing a first level of inferencing ( 308 ), by clustering the received images into one or more classes using the extracted features, the step of clustering comprising: determining an optimal number of the one or more classes using a Silhouette coefficient ( 308 a ); identifying the one or more classes based on similarity detected between the received images ( 308 b ) by performing at least one of: computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images ( 308 b - 1 ); computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP) ( 308 b - 2 ); and computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes ( 308 b - 3 );
determining an optimal number of the one or more classes using a Silhouette coefficient ( 308 a );
identifying the one or more classes based on similarity detected between the received images ( 308 b ) by performing at least one of: computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images ( 308 b - 1 ); computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP) ( 308 b - 2 ); and computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes ( 308 b - 3 );
computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images ( 308 b - 1 );
computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP) ( 308 b - 2 ); and
computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes ( 308 b - 3 );
wherein the number of the one or more classes equals the determined optimal number; and validating the quality of the one or more classes using one or more of an Normalized Mutual Information (NMI) score, a Rand Index and a purity measure ( 308 c ); and
validating the quality of the one or more classes using one or more of an Normalized Mutual Information (NMI) score, a Rand Index and a purity measure ( 308 c ); and
associating the one or more classes with a tag based on the context under consideration ( 310 ).
The processor implemented method of claim 1 , wherein the step of receiving comprises obtaining at least some metadata associated with the received images; and updating the metadata associated with the received images based on the associated tags.
The processor implemented method of claim 1 , wherein the step of extracting features from the received images is preceded by a step of pre-processing comprising at least one of (i) a first level of pre-processing, to enhance quality of the received images, by performing one or more of normalization, Principal Components Analysis (PCA) whitening, brightness correction, standardization and segmentation; and (ii) a second level of pre-processing, to adapt the received images for forward passing to the neural network model, by performing one or more of rotation, cropping, shifting, scaling and zooming.
The processor implemented method of claim 2 , wherein the step of extracting features from the received images comprises one or more of appending the features extracted by the neural network model, with additional features including morphological features and color related features to derive a master feature set; and compressing the master feature set using dimensionality reduction methods.
The processor implemented method of claim 1 , wherein the step of performing the first level of inferencing is performed within one or more of the one or more classes.
The processor implemented method of claim 4 , further comprising the step of performing a second level of inferencing by classifying the one or more classes into one or more sub-classes ( 312 ), the step of classifying comprising: obtaining a plurality of pre-trained tagger models associated with one or more sub-classes corresponding to one or more classes pertaining to the context under consideration, wherein the pre-trained tagger models are trained by the master feature set ( 312 a ); classifying the one or more classes from the first level of inferencing into the one or more sub-classes based on the plurality of pre-trained tagger models ( 312 b ); computing a confidence level for the classified one or more sub-classes ( 312 c ); re-training the plurality of pre-trained tagger models with the received images associated with the confidence level below a pre-defined threshold to obtain a plurality of classification models ( 312 d ); and creating a knowledge ontology of the classifications models based on the one or more classes, the one or more sub-classes therein and the inter-relationships thereof ( 312 e ).
obtaining a plurality of pre-trained tagger models associated with one or more sub-classes corresponding to one or more classes pertaining to the context under consideration, wherein the pre-trained tagger models are trained by the master feature set ( 312 a );
classifying the one or more classes from the first level of inferencing into the one or more sub-classes based on the plurality of pre-trained tagger models ( 312 b );
computing a confidence level for the classified one or more sub-classes ( 312 c );
re-training the plurality of pre-trained tagger models with the received images associated with the confidence level below a pre-defined threshold to obtain a plurality of classification models ( 312 d ); and
creating a knowledge ontology of the classifications models based on the one or more classes, the one or more sub-classes therein and the inter-relationships thereof ( 312 e ).
The processor implemented method of claim 6 , wherein the step of re-training the plurality of pre-trained tagger models is preceded by evaluating the received images associated with the confidence level below the pre-defined threshold with the associated metadata.
The processor implemented method of claim 7 further comprising storing the received images and the metadata associated thereof; the one or more classes; the one or more sub-classes, the plurality of classification models and the knowledge ontology ( 314 ).
The processor implemented method of claim 8 , wherein the step of identifying and transmitting at least a subset of the received images comprises performing one or more of: determining whether the received images are valid based on an entropy value associated thereof; and comparing the received images with a pre-determined number of previous images to determine whether the received images fit into the one or more sub-classes associated thereof, wherein the pre-determined number of previous images is the adaptive threshold based on the context under consideration.
determining whether the received images are valid based on an entropy value associated thereof; and
comparing the received images with a pre-determined number of previous images to determine whether the received images fit into the one or more sub-classes associated thereof, wherein the pre-determined number of previous images is the adaptive threshold based on the context under consideration.
A system ( 200 ) comprising: one or more internal data storage devices operatively coupled to one or more hardware processors for storing instructions configured for execution by the one or more hardware processors, the instructions being comprised in: an input module ( 120 a ) configured to: receive a plurality of images, at one or more time intervals, pertaining to a context under consideration, the received images being correlated and associated with at least one of a spatial and temporal information; and identify and transmit, at the one or more time intervals, at least a subset of the received images based on the spatial or temporal information and an adaptive threshold; a feature extractor ( 120 c, 130 c ) configured to extract features from the received images, by forward passing the received images through a neural network model pre-trained on a dataset of a plurality of images pertaining to varied contexts; a clustering module ( 130 e ) configured to perform a first level of inferencing by clustering the received images into one or more classes using the extracted features, the step of clustering comprising: determining an optimal number of the one or more classes using a Silhouette coefficient; identifying the one or more classes based on similarity detected between the received images by performing at least one of: computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images; computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP); and computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes; wherein the number of the one or more classes equals the determined optimal number; and validating the quality of the one or more classes using one or more of an Normalized Mutual Information (NMI) score, a Rand Index and a purity measure; and associating the one or more classes with a tag based on the context under consideration.
one or more internal data storage devices operatively coupled to one or more hardware processors for storing instructions configured for execution by the one or more hardware processors, the instructions being comprised in:
an input module ( 120 a ) configured to: receive a plurality of images, at one or more time intervals, pertaining to a context under consideration, the received images being correlated and associated with at least one of a spatial and temporal information; and identify and transmit, at the one or more time intervals, at least a subset of the received images based on the spatial or temporal information and an adaptive threshold;
receive a plurality of images, at one or more time intervals, pertaining to a context under consideration, the received images being correlated and associated with at least one of a spatial and temporal information; and
identify and transmit, at the one or more time intervals, at least a subset of the received images based on the spatial or temporal information and an adaptive threshold;
a feature extractor ( 120 c, 130 c ) configured to extract features from the received images, by forward passing the received images through a neural network model pre-trained on a dataset of a plurality of images pertaining to varied contexts;
a clustering module ( 130 e ) configured to perform a first level of inferencing by clustering the received images into one or more classes using the extracted features, the step of clustering comprising: determining an optimal number of the one or more classes using a Silhouette coefficient; identifying the one or more classes based on similarity detected between the received images by performing at least one of: computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images; computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP); and computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes;
determining an optimal number of the one or more classes using a Silhouette coefficient;
identifying the one or more classes based on similarity detected between the received images by performing at least one of: computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images; computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP); and computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes;
computing a first distance measure represented by a spherical distance of each of the received images with every other image in the received images;
computing a likelihood of each image in the received images to belong to a class using a class distribution based on a Maximum A Posteriori probability (MAP); and
computing a second distance measure represented by a spherical distance between each of the received images and a centroid of each of the one or more classes;
wherein the number of the one or more classes equals the determined optimal number; and validating the quality of the one or more classes using one or more of an Normalized Mutual Information (NMI) score, a Rand Index and a purity measure; and
validating the quality of the one or more classes using one or more of an Normalized Mutual Information (NMI) score, a Rand Index and a purity measure; and
associating the one or more classes with a tag based on the context under consideration.
The system of claim 10 , wherein the input module is further configured to obtain at least some metadata associated with the received images; and update the metadata associated with the received images based on the associated tags.
The system of claim 11 further comprising a pre-processing module ( 120 b, 130 b ) configured to pre-process the received images, prior to extracting the features, by performing at least one of (i) a first level of pre-processing, to enhance quality of the received images, by performing one or more of normalization, Principal Components Analysis (PCA) whitening, brightness correction, standardization and segmentation; and (ii) a second level of pre-processing, to adapt the received images for forward passing to the neural network model, by performing one or more of rotation, cropping, shifting, scaling and zooming.
The system of claim 12 , wherein the feature extractor is further configured to append the features extracted by the neural network model, with additional features including morphological features and color related features to derive a master feature set; and compress the master feature set using dimensionality reduction methods.
The system of claim 10 , wherein the clustering module is further configured to perform the first level of inferencing within one or more of the one or more classes.
The system of claim 13 further comprising a classification module ( 130 f ) configured to perform a second level of inferencing by classifying the one or more classes into one or more sub-classes, the step of classifying comprising: obtaining a plurality of pre-trained tagger models ( 130 a ) associated with one or more sub-classes corresponding to one or more classes pertaining to the context under consideration, wherein the pre-trained tagger models are trained by the master feature set; classifying the one or more classes from the first level of inferencing into the one or more sub-classes based on the plurality of pre-trained tagger models; computing a confidence level for the classified one or more sub-classes; re-training the plurality of pre-trained tagger models with the images associated with the confidence level below a pre-defined threshold to obtain a plurality of classification models ( 120 d, 130 d ); and creating a knowledge ontology of the classifications models based on the one or more classes, the one or more sub-classes therein and the inter-relationships thereof.
obtaining a plurality of pre-trained tagger models ( 130 a ) associated with one or more sub-classes corresponding to one or more classes pertaining to the context under consideration, wherein the pre-trained tagger models are trained by the master feature set;
classifying the one or more classes from the first level of inferencing into the one or more sub-classes based on the plurality of pre-trained tagger models;
computing a confidence level for the classified one or more sub-classes;
re-training the plurality of pre-trained tagger models with the images associated with the confidence level below a pre-defined threshold to obtain a plurality of classification models ( 120 d, 130 d ); and
creating a knowledge ontology of the classifications models based on the one or more classes, the one or more sub-classes therein and the inter-relationships thereof.
The system of claim 15 , wherein the classification module is further configured to evaluate the received images associated with the confidence level below the pre-defined threshold with the associated metadata, prior to re-training the pre-trained tagger models.
The system of claim 16 further comprising a database ( 130 g ) configured to store the received images and the metadata associated thereof; the one or more classes; the one or more sub-classes, the plurality of classificatio
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