INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND PROGRAM
First Claim
Patent Images
1. An information processing device comprising:
- feature amount extracting means configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene;
clustering means configured to use cluster information that is the information of said cluster obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs;
highlight label generating means configured to generate a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations; and
highlight detector learning means configured to perform learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence.
1 Assignment
0 Petitions
Accused Products
Abstract
An information processing device includes a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; a clustering unit configured to use cluster information that is the information of the cluster obtained by performing cluster learning; a highlight label generating unit configured to generate a highlight label sequence; and a highlight detector learning unit configured to perform learning of the highlight detector.
47 Citations
28 Claims
-
1. An information processing device comprising:
-
feature amount extracting means configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; clustering means configured to use cluster information that is the information of said cluster obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs; highlight label generating means configured to generate a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations; and highlight detector learning means configured to perform learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
-
-
12. An information processing method using an information processing device, comprising the steps of:
-
extracting the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; using cluster information that is the information of said cluster obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs; generating a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations; and performing learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence.
-
-
13. A program causing a computer to serve as:
-
feature amount extracting means configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; clustering means configured to use cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs; highlight label generating means configured to generate a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations; and highlight detector learning means configured to perform learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence.
-
-
14. An information processing device comprising:
-
obtaining means configured to obtain said highlight detector obtained by extracting the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene, using cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs, generating a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations, and performing learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence; feature amount extracting means configured to extract the feature amount of each frame of an image of a content for highlight detection of interest that is a content from which a highlight scene is to be detected; clustering means configured to convert the time sequence of the feature amount of said content for highlight detection of interest into said code sequence by subjecting the feature amount of each frame of said content for highlight detection of interest to clustering into one cluster of said plurality of clusters using said cluster information; maximum likelihood state sequence estimating means configured to estimate the maximum likelihood state sequence that is a state sequence causing state transition to occur where likelihood is the highest that a label sequence for detection that is a pair of said code sequence obtained from said content for highlight detection of interest, and the highlight label sequence of a highlight label representing a highlight scene or non-highlight scene will be observed in said highlight detector; highlight scene detecting means configured to detect the frame of a highlight scene from said content for highlight detection of interest based on the observation probability of said highlight label of each state of a highlight relation state sequence that is said maximum likelihood state sequence obtained from said label sequence for detection; and digest contents generating means configured to generate a digest content that is the digest of said content for highlight detection of interest using the frame of said highlight scene. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22)
-
-
23. An information processing method using an information processing device, comprising the steps of:
-
obtaining said highlight detector to be obtained by extracting the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene, using cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs, generating a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations, and performing learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence; extracting the feature amount of each frame of an image of a content for highlight detection of interest that is a content from which a highlight scene is to be detected; converting the time sequence of the feature amount of said content for highlight detection of interest into said code sequence by subjecting the feature amount of each frame of said content for highlight detection of interest to clustering into one cluster of said plurality of clusters using said cluster information; estimating the maximum likelihood state sequence that is a state sequence causing state transition to occur where likelihood is the highest that a label sequence for detection that is a pair of said code sequence obtained from said content for highlight detection of interest, and the highlight label sequence of a highlight label representing a highlight scene or non-highlight scene will be observed in said highlight detector; detecting the frame of a highlight scene from said content for highlight detection of interest based on the observation probability of said highlight label of each state of a highlight relation state sequence that is said maximum likelihood state sequence obtained from said label sequence for detection; and generating a digest content that is the digest of said content for highlight detection of interest using the frame of said highlight scene.
-
-
24. A program causing a computer to serve as:
-
obtaining means configured to obtain said highlight detector obtained by extracting the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene, using cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs, generating a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations, and performing learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence; feature amount extracting means configure to extract the feature amount of each frame of an image of a content for highlight detection of interest that is a content from which a highlight scene is to be detected; clustering means configured to convert the time sequence of the feature amount of said content for highlight detection of interest into said code sequence by subjecting the feature amount of each frame of said content for highlight detection of interest to clustering into one cluster of said plurality of clusters using said cluster information; maximum likelihood state sequence estimating means configured to estimate the maximum likelihood state sequence that is a state sequence causing state transition to occur where likelihood is the highest that a label sequence for detection that is a pair of said code sequence obtained from said content for highlight detection of interest, and the highlight label sequence of a highlight label representing a highlight scene or non-highlight scene will be observed in said highlight detector; highlight scene detecting means configured to detect the frame of a highlight scene from said content for highlight detection of interest based on the observation probability of said highlight label of each state of a highlight relation state sequence that is said maximum likelihood state sequence obtained from said label sequence for detection; and digest contents generating means configured to generate a digest content that is the digest of said content for highlight detection of interest using the frame of said highlight scene.
-
-
25. An information processing device comprising:
-
a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; a clustering unit configured to use cluster information that is the information of said cluster obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs; a highlight label generating unit configured to generate a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations; and a highlight detector learning unit configured to perform learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence.
-
-
26. A program causing a computer to serve as:
-
a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; a clustering unit configured to use cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs; a highlight label generating unit configured to generate a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations; and a highlight detector learning unit configured to perform learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence.
-
-
27. An information processing device comprising:
-
an obtaining unit configured to obtain said highlight detector obtained by extracting the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene, using cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs, generating a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations, and performing learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence; a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for highlight detection of interest that is a content from which a highlight scene is to be detected; a clustering unit configured to convert the time sequence of the feature amount of said content for highlight detection of interest into said code sequence by subjecting the feature amount of each frame of said content for highlight detection of interest to clustering into one cluster of said plurality of clusters using said cluster information; a maximum likelihood state sequence estimating unit configured to estimate the maximum likelihood state sequence that is a state sequence causing state transition to occur where likelihood is the highest that a label sequence for detection that is a pair of said code sequence obtained from said content for highlight detection of interest, and the highlight label sequence of a highlight label representing a highlight scene or non-highlight scene will be observed in said highlight detector; a highlight scene detecting unit configured to detect the frame of a highlight scene from said content for highlight detection of interest based on the observation probability of said highlight label of each state of a highlight relation state sequence that is said maximum likelihood state sequence obtained from said label sequence for detection; and a digest contents generating unit configured to generate a digest content that is the digest of said content for highlight detection of interest using the frame of said highlight scene.
-
-
28. A program causing a computer to serve as:
-
an obtaining unit configured to obtain said highlight detector obtained by extracting the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene, using cluster information that is the information of said clusters obtained by performing cluster learning for extracting the feature amount of each frame of an image of a content for learning that is a content to be used for cluster learning for dividing feature amount space that is the space of said feature amount into a plurality of clusters, and dividing said feature amount space into a plurality of clusters using the feature amount of each frame of said content for learning to subject the feature amount of each frame of said content for detector learning of interest to clustering into one cluster of said plurality of clusters, thereby converting the time sequence of the feature amount of said content for detector learning of interest into the code sequence of a code representing a cluster to which the feature amount of said content for detector learning of interest belongs, generating a highlight label sequence regarding said content for detector learning of interest by labeling each frame of said content for detector learning of interest using a highlight label representing whether or not said highlight scene in accordance with the user'"'"'s operations, and performing learning of said highlight detector which is a state transition probability model stipulated by state transition probability that a state will proceed, and observation probability that a predetermined observation value will be observed from said state, using a label sequence for learning that is a pair of said code sequence obtained from said content for detector learning of interest, and said highlight label sequence; a feature amount extracting unit configure to extract the feature amount of each frame of an image of a content for highlight detection of interest that is a content from which a highlight scene is to be detected; a clustering unit configured to convert the time sequence of the feature amount of said content for highlight detection of interest into said code sequence by subjecting the feature amount of each frame of said content for highlight detection of interest to clustering into one cluster of said plurality of clusters using said cluster information; a maximum likelihood state sequence estimating unit configured to estimate the maximum likelihood state sequence that is a state sequence causing state transition to occur where likelihood is the highest that a label sequence for detection that is a pair of said code sequence obtained from said content for highlight detection of interest, and the highlight label sequence of a highlight label representing a highlight scene or non-highlight scene will be observed in said highlight detector; a highlight scene detecting unit configured to detect the frame of a highlight scene from said content for highlight detection of interest based on the observation probability of said highlight label of each state of a highlight relation state sequence that is said maximum likelihood state sequence obtained from said label sequence for detection; and a digest contents generating unit configured to generate a digest content that is the digest of said content for highlight detection of interest using the frame of said highlight scene.
-
Specification