Abstract: A method may include obtaining current sensor information that corresponds to one or more sensors of a wheelchair and that indicates a current state of the wheelchair and its surrounding environment. The method may further include obtaining current situation information that indicates a current situation of a wheelchair and its corresponding user. In addition, the method may include determining current solution information that corresponds to a current solution to help navigate the current situation. The current solution information may be determined based on one or more of the following: the current situation information and the current sensor information. The method may additionally include implementing the current solution based on the current solution information.

Abstract: A method may include obtaining current sensor information that corresponds to one or more sensors of a wheelchair and that indicates a current state of the wheelchair and its surrounding environment. The method may further include obtaining current situation information that indicates a current situation of a wheelchair and its corresponding user. In addition, the method may include determining current solution information that corresponds to a current solution to help navigate the current situation. The current solution information may be determined based on one or more of the following: the current situation information and the current sensor information. The method may additionally include implementing the current solution based on the current solution information.

Abstract: A computer-readable recording medium storing a program for causing a computer to execute a procedure for managing a system that retrieves a image corresponding to a first type specified by a retrieval request received from a terminal device and another image corresponding to a second type associated with the first type, from an image database storing a plurality of images associated with at least a number of a plurality of types, the procedure includes: referring to a learning state representing whether feature values of images corresponding to each of the plurality of the types have been obtained; retrieving an unlearned type in which the feature value has not been obtained, among the plurality of types; specifying an association type associated with the unlearned type; and outputting the unlearned type in accordance with the learning state of the association type.

Abstract: A computer-readable recording medium storing a program for causing a computer to execute an image accumulating procedure, the procedure includes: specifying a second image similar to a first image that is associated with text information; displaying the second image in an identifiable manner, and the text information on a display device; and storing the text information associated with image information that is related to the second image, based on instruction information that instructs a use of the text information with respect to the second image.

Abstract: An image processing apparatus includes: a storage configured to store, for a plurality of areas in a target image, attributes and patterns associated with the attributes, each the attributes being assigned to each the plurality of areas, and each the pattern being created from areas having an identical attribute among the attributes; and a processor configured to: obtain an area that is specified in the target image and an attribute that is assigned to the specified area, search the storage for a pattern based on similarity between the pattern and a pattern created from a certain number of areas having the assigned attribute; and search for an area in the target image based on similarity between the area and the certain number of areas in accordance with the searched pattern.

Abstract: An image retrieval method includes reading from a first database a first image group mapped to an object as a retrieval target, and extracting an image feature quantity of a plurality of images of the first image group. A second image group mapped to an object as a search key is read from a second database and an image feature quantity of a plurality of images of the second image group is extracted. Similarity is determined between the first image group and the second image group, based on the image feature quantity of the plurality of images of the first image group and the image feature quantity of the plurality of images of the second image group. On an output device, as retrieval results, the plurality of images of the first image group are displayed in order of similarity of the object as the retrieval target based on determined similarity.

Abstract: Embodiments describe an image retrieval apparatus. The image retrieval apparatus includes an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.

Abstract: An image identifying device includes: a setting unit which sets a section having at least one image in a video; a first recognizing unit which calculates a plurality of feature amounts related to at least the one image and which acquires a plurality of identification results corresponding to each of the feature amounts from an identifier which may identify a plurality of objects belonging to a first category; a selecting unit which selects, based on the identification results, a second category of a third category; and a second recognizing unit which calculates another feature amount related to an image included in another section and acquires another identification result corresponding to the feature amount from another identifier which may identify the objects included in the second category.

Abstract: An image identifying device includes: a setting unit which sets a section having at least one image in a video; a first recognizing unit which calculates a plurality of feature amounts related to at least the one image and which acquires a plurality of identification results corresponding to each of the feature amounts from an identifier which may identify a plurality of objects belonging to a first category; a selecting unit which selects, based on the identification results, a second category of a third category; and a second recognizing unit which calculates another feature amount related to an image included in another section and acquires another identification result corresponding to the feature amount from another identifier which may identify the objects included in the second category.

Abstract: A computer-readable storage medium storing an image processing program that causes a computer to execute a process includes acquiring the same object identification information included in a plurality of image data items by referring to a storage unit that stores each of the image data items, object identification information that identifies an object included in the image data item, and a location information item that identifies a location of the object in the image data item in association with one another; acquiring the location of the object identified by the acquired object identification information in each of the image data items by referring to the storage unit; computing a difference between the acquired location information items; comparing the difference between the location information items with a predetermined location threshold value; and determining whether the image data items are to be in the same group.

Abstract: An image processing apparatus includes: a storage configured to store, for a plurality of areas in a target image, attributes and patterns associated with the attributes, each the attributes being assigned to each the plurality of areas, and each the pattern being created from areas having an identical attribute among the attributes; and a processor configured to: obtain an area that is specified in the target image and an attribute that is assigned to the specified area, search the storage for a pattern based on similarity between the pattern and a pattern created from a certain number of areas having the assigned attribute; and search for an area in the target image based on similarity between the area and the certain number of areas in accordance with the searched pattern.

Abstract: Embodiments describe an image retrieval apparatus. The image retrieval apparatus includes an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.

Abstract: A television personal computer 40, when saving video data 43 by recording a video content, extracts a section including a predetermined type of scene from within the video content, then specifies, when accepting a skip instruction from an operator during reproduction of the video content, a resuming point based on a relationship between the section extracted from the video content and the reproducing point, and resumes the reproduction of the video content from the resuming point.

Abstract: This invention discloses an image retrieval apparatus. The image retrieval apparatus comprises an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.

Abstract: A playback apparatus that divides the content into scenes; specifies a scene played back after a fast-forward operation or a rewind; calculates a first feature value of the scene specified by the specifying unit; divides content different from the video content into scenes and calculates second feature values, each of which corresponds to one of the scenes obtained by the second feature-value calculation unit; compares the first feature value with the second feature values and extracts a scene whose second feature value differs from the first feature value by a predetermined threshold or less; and outputs the scene extracted by the extraction unit.

Abstract: An image identifying device includes: a setting unit which sets a section having at least one image in a video; a first recognizing unit which calculates a plurality of feature amounts related to at least the one image and which acquires a plurality of identification results corresponding to each of the feature amounts from an identifier which may identify a plurality of objects belonging to a first category; a selecting unit which selects, based on the identification results, a second category of a third category ; and a second recognizing unit which calculates another feature amount related to an image included in another section and acquires another identification result corresponding to the feature amount from another identifier which may identify the objects included in the second category.

Abstract: A difference emphasizing apparatus aligns a first three-dimensional model and a second three-dimensional model in orientation and position in accordance with a predetermined rule, and gets data of respective apices of the first three-dimensional model and the second three-dimensional model. Based on the gotten data, the apparatus finds a corresponding point on the first three-dimensional model, which corresponds to the apex of the second three-dimensional model in a direction of a particular axis. When the corresponding point is detected, the apparatus calculates a difference between the first three-dimensional model and the second three-dimensional model in the direction of the particular axis based on the corresponding point and the apex of the second three-dimensional model. The apparatus enlarges the difference in the direction of the particular axis, and calculates a position of the apex of the second three-dimensional model after the enlargement.

Abstract: A rule discovery program which enables discovery of a rule by automatically searching for a feature portion which is effective for generation of a rule in multimedia data. When sample data items are inputted, a search unit selects evaluation positions on a data array constituting each of the sample data items so as to generate a selection pattern. Next, an integration unit integrates the evaluation positions in the selection pattern so as to generate a predetermined number of evaluation regions, and a rule-candidate generation unit generates a rule candidate indicating a relationship common to the sample data items. A rule-candidate evaluation unit evaluates the generated rule candidate, and an output unit outputs, as a rule, the rule candidate when the rule candidate satisfies a predetermined criterion.

Abstract: A computer-readable storage medium storing an image processing program that causes a computer to execute a process includes acquiring the same object identification information included in a plurality of image data items by referring to a storage unit that stores each of the image data items, object identification information that identifies an object included in the image data item, and a location information item that identifies a location of the object in the image data item in association with one another; acquiring the location of the object identified by the acquired object identification information in each of the image data items by referring to the storage unit; computing a difference between the acquired location information items; comparing the difference between the location information items with a predetermined location threshold value; and determining whether the image data items are to be in the same group.

Abstract: This invention discloses an image retrieval apparatus. The image retrieval apparatus comprises an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.