Abstract: An output value of a first neural network for input data is obtained in correspondence with each category. An output value of a second neural network generated by changing a designated unit in the first neural network is obtained for the input data in correspondence with each category. For each category, change information representing a change in the output value is obtained. Information representing contribution of the designated unit are output to a display device based on the change information.

Abstract: An apparatus acquires a video image from a surveillance camera 2 and displays an image based on the video image on a display 3. The apparatus 1 records the video image acquired from the surveillance camera 2 in a recording unit 105. The apparatus 1 includes an abnormality detection unit 106 configured to detect an abnormality from the video image, an attention period determination unit 107 configured to determine an attention period based on a period from a start to an end of a detected abnormality, and a displaying unit 102 configured to display a video image acquired from the surveillance camera 2 as it is on a display 3 until the abnormality being detected ends, and, when the abnormality being detected ends, acquire, from a recording unit 105, a recorded video image recorded in a period corresponding to the attention period and play back the acquired recorded video image.

Abstract: An apparatus acquires a video image from a surveillance camera 2 and displays an image based on the video image on a display 3. The apparatus 1 records the video image acquired from the surveillance camera 2 in a recording unit 105. The apparatus 1 includes an abnormality detection unit 106 configured to detect an abnormality from the video image, an attention period determination unit 107 configured to determine an attention period based on a period from a start to an end of a detected abnormality, and a displaying unit 102 configured to display a video image acquired from the surveillance camera 2 as it is on a display 3 until the abnormality being detected ends, and, when the abnormality being detected ends, acquire, from a recording unit 105, a recorded video image recorded in a period corresponding to the attention period and play back the acquired recorded video image.

Abstract: A recognition training apparatus according to the present invention includes a generation unit configured to generate relevance between a specific domain and a candidate of a recognition target based on ontology information expressing a concept structure of the specific domain, a selection unit configured to select the recognition target from the candidate of the recognition target based on the relevance generated by the generation unit, and a training unit configured to train a recognizer using training data regarding the recognition target selected by the selection unit.

Abstract: A plurality of random number sequences are generated using a plurality of random number seeds stored in a memory, and a plurality of base vectors are generated based on the plurality of random number sequences. A transformation source vector is transformed into a lower-dimensional vector by performing random projection for the transformation source vector using the plurality of generated base vectors.

Abstract: An output value of a first neural network for input data is obtained in correspondence with each category. An output value of a second neural network generated by changing a designated unit in the first neural network is obtained for the input data in correspondence with each category. For each category, change information representing a change in the output value is obtained. Information representing contribution of the designated unit are output to a display device based on the change information.

Abstract: A recognition training apparatus according to the present invention includes a generation unit configured to generate relevance between a specific domain and a candidate of a recognition target based on ontology information expressing a concept structure of the specific domain, a selection unit configured to select the recognition target from the candidate of the recognition target based on the relevance generated by the generation unit, and a training unit configured to train a recognizer using training data regarding the recognition target selected by the selection unit.

Abstract: A holding unit of an information processing apparatus holds a classification model and characteristic information for each of a plurality of groups acquired by dividing a plurality of feature values extracted from a plurality of training data pieces belonging to a specific class. Then, a feature extraction unit extracts a feature value from input data, and a selection unit selects one or more group(s) from the plurality of groups based on this extracted feature value and the characteristic information held by the holding unit. Then, a determination unit determines whether the input data belongs to the specific class with use of the classification model(s) corresponding to the selected group(s).

Abstract: A plurality of random number sequences are generated using a plurality of random number seeds stored in a memory, and a plurality of base vectors are generated based on the plurality of random number sequences. A transformation source vector is transformed into a lower-dimensional vector by performing random projection for the transformation source vector using the plurality of generated base vectors.

Abstract: A hash value generation apparatus that generates a hash value for identifying unknown data as belonging to a specified class or an unspecified class, includes a generation unit configured to generate hash function information including a hash function based on a specified feature amount of data belonging to the specified class, a conversion unit configured to convert the specified feature amount into a hash value based on the generated hash function information, and a storage unit configured to store the hash value obtained by the conversion as a normal hash value in association with the hash function information.