Abstract: To search for a similar image more accurately, an image search system acquires a plurality of representative vectors, which are generated based on a plurality of feature vectors each indicating a feature of an image and included in a feature vector space, calculates a scalar value indicating a degree of similarity between each of the plurality of feature vectors and one of the representative vectors corresponding to the feature vector, calculates, for each of images, a feature value indicating a feature that depends on the representative vector based on the scalar value, and creates a search index associated with the calculated feature value.

Abstract: In order to improve accuracy for detecting presence or absence of a target object. A time-series segmentation unit 102 creates first time-series data by segmenting processing target data into each frame of “n” time zones. Each of first determination units 103 creates “m” second time-series data by determining each frame of the first time-series data using “m” models having different characteristics. A second determination unit 104 creates a second determination result as a presence probability of the target object for a set of second time-series data including n×m data.

Abstract: To calculate quickly a similarity to a query vector even when the number of dimensions of a target vector is large. The similarity calculation system is configured to: acquire a plurality of first target vectors; generate a plurality of second target vectors having a smaller number of dimensions than the plurality of first target vectors by subjecting each of the plurality of first target vectors to predetermined processing; acquire, based on the plurality of second target vectors, a plurality of clusters each including at least one of the plurality of first target vectors and at least one of the plurality of the second target vectors, each of the at least one second target vector corresponding to one of the at least one first target vector; and calculate a similarity between each of the at least one first target vector belonging to one or more clusters selected based on a given query vector having the same number of dimensions as the at least one first target vector, and the given query vector.

Abstract: In order to improve accuracy for detecting presence or absence of a target object. A time-series segmentation unit 102 creates first time-series data by segmenting processing target data into each frame of “n” time zones. Each of first determination units 103 creates “m” second time-series data by determining each frame of the first time-series data using “m” models having different characteristics. A second determination unit 104 creates a second determination result as a presence probability of the target object for a set of second time-series data including n×m data.

Abstract: To search for a similar image more accurately, an image search system acquires a plurality of representative vectors, which are generated based on a plurality of feature vectors each indicating a feature of an image and included in a feature vector space, calculates a scalar value indicating a degree of similarity between each of the plurality of feature vectors and one of the representative vectors corresponding to the feature vector, calculates, for each of images, a feature value indicating a feature that depends on the representative vector based on the scalar value, and creates a search index associated with the calculated feature value.

Abstract: Provided is a similarity calculation system for equalizing the time for calculating a similarity between target vectors and a query vector. The similarity calculation system includes target vector acquisition part for acquiring a plurality of target vectors, and clustering part for clustering the plurality of target vectors based on a calculation amount to be estimated for each of the plurality of target vectors, the calculation amount being estimated when calculating a similarity between each of the plurality of target vectors and a given reference query vector, so that a difference in total calculation amount for a similarity between all of the target vectors belonging to each of a plurality of clusters and the given reference query vector among the plurality of clusters decreases.

Abstract: An image search device is configured to: obtain a plurality of query feature vectors each indicating a local feature of an image serving as a query; select a plurality of image feature vectors respectively corresponding to the plurality of query feature vectors based on proximities between the plurality of image feature vectors respectively indicating a plurality of local features of each of a plurality of search target images to be searched, and the plurality of query feature vectors; generate an image score of the search target image based on a total sum of score elements each corresponding to the proximities between the selected plurality of image feature vectors and the query feature vectors corresponding to the selected image feature vectors; and select at least one of the plurality of search target images based on the image score.

Abstract: An image search device is configured to: obtain a plurality of query feature amounts each indicating a local feature of an image serving as a query; select image feature amounts respectively corresponding to the respective query feature amounts so that one image feature amount is selected relative to one of the plurality of query feature amounts for each of a plurality of images to be searched from image feature amounts respectively indicating local features of each of the images to be searched; generate a score indicating a degree of similarity between each of the plurality of images to be searched and the image serving as the query based on the image feature amounts selected relative to the plurality of query feature amounts; and select at least one image from the plurality of images to be searched based on the score generated for the image.

Abstract: An image search device includes a common memory and a plurality of parallel processors for executing a same instruction. The image search device transfers, from storage, a plurality of representative feature vectors, which respectively represent a plurality of clusters including a plurality of image feature vectors, stores, in the common memory, one or more query feature vectors extracted from an image serving as a query, calculates a distance between the plurality of transferred representative feature vectors and the query feature vector using the plurality of parallel processors, and selects one or more of a plurality of images based on a distance between the plurality of image feature vectors, which belong to the cluster selected by the calculated distance, and the query feature vector.

Abstract: Provided is a similarity calculation system for equalizing the time for calculating a similarity between target vectors and a query vector. The similarity calculation system includes target vector acquisition part for acquiring a plurality of target vectors, and clustering part for clustering the plurality of target vectors based on a calculation amount to be estimated for each of the plurality of target vectors, the calculation amount being estimated when calculating a similarity between each of the plurality of target vectors and a given reference query vector, so that a difference in total calculation amount for a similarity between all of the target vectors belonging to each of a plurality of clusters and the given reference query vector among the plurality of clusters decreases.

Abstract: To calculate quickly a similarity to a query vector even when the number of dimensions of a target vector is large. The similarity calculation system is configured to: acquire a plurality of first target vectors; generate a plurality of second target vectors having a smaller number of dimensions than the plurality of first target vectors by subjecting each of the plurality of first target vectors to predetermined processing; acquire, based on the plurality of second target vectors, a plurality of clusters each including at least one of the plurality of first target vectors and at least one of the plurality of the second target vectors, each of the at least one second target vector corresponding to one of the at least one first target vector; and calculate a similarity between each of the at least one first target vector belonging to one or more clusters selected based on a given query vector having the same number of dimensions as the at least one first target vector, and the given query vector.

Abstract: An image search device is configured to: obtain a plurality of query feature vectors each indicating a local feature of an image serving as a query; select a plurality of image feature vectors respectively corresponding to the plurality of query feature vectors based on proximities between the plurality of image feature vectors respectively indicating a plurality of local features of each of a plurality of search target images to be searched, and the plurality of query feature vectors; generate an image score of the search target image based on a total sum of score elements each corresponding to the proximities between the selected plurality of image feature vectors and the query feature vectors corresponding to the selected image feature vectors; and select at least one of the plurality of search target images based on the image score.

Abstract: An image search device includes a common memory and a plurality of parallel processors for executing a same instruction. The image search device transfers, from storage, a plurality of representative feature vectors, which respectively represent a plurality of clusters including a plurality of image feature vectors, stores, in the common memory, one or more query feature vectors extracted from an image serving as a query, calculates a distance between the plurality of transferred representative feature vectors and the query feature vector using the plurality of parallel processors, and selects one or more of a plurality of images based on a distance between the plurality of image feature vectors, which belong to the cluster selected by the calculated distance, and the query feature vector.

Abstract: An image search device is configured to: obtain a plurality of query feature amounts each indicating a local feature of an image serving as a query; select image feature amounts respectively corresponding to the respective query feature amounts so that one image feature amount is selected relative to one of the plurality of query feature amounts for each of a plurality of images to be searched from image feature amounts respectively indicating local features of each of the images to be searched; generate a score indicating a degree of similarity between each of the plurality of images to be searched and the image serving as the query based on the image feature amounts selected relative to the plurality of query feature amounts; and select at least one image from the plurality of images to be searched based on the score generated for the image.