2574 results about "Image retrieval" patented technology

System and methods for querying digital image archives containing digital photographs and/or videos (collectively, "digital images"). The digital images are indexed in accordance with a plurality of recorded parameters including time, date and geographic location data (altitude and longitude), as well as image data such as lens focal length, auto focus distance, shutter speed, exposure duration, aperture setting, frame number, image quality, flash status and light meter readings, which are used for searching a database consisting of the digital images. These images are preferably generated by an image capturing system which is capable of measuring and recording a plurality of parameters with each captured digital image. The image retrieval system allows a querying user to search the image archive by formulating one or more of a plurality of query types which are based on the recorded parameters, and then retrieve and display those images having the specified parameters.

A method and apparatus for video retrieval and cueing that automatically detects human faces in the video and identifies face-specific video frames so as to allow retrieval and viewing of person-specific video segments. In one embodiment, the method locates human faces in the video, stores the time stamps associated with each face, displays a single image associated with each face, matches each face against a database, computes face locations with respect to a common 3D coordinate system, and provides a means of displaying: 1) information retrieved from the database associated with a selected person or people, 2) path of travel associated with a selected person or people 3) interaction graph of people in video, 4) video segments associated with each person and/or face. The method may also provide the ability to input and store text annotations associated with each person, face, and video segment, and the ability to enroll and remove people from database. The videos of non-human objects may be processed in a similar manner. Because of the rules governing abstracts, this abstract should not be used to construe the claims.

A method and system to help photographers to take better quality pictures of landmarks and scenes are disclosed. A user is guided with examples of existing quality images, which are extracted from a database, of the same or similar landmarks or scenes. The method includes taking a query photograph that may include an image associated with a GPS location and other metadata, and using information extracted from the image to retrieve existing, similar images. The images retrieved may be ordered according to different criteria. When a user selects one as a model image, the user is provided with assistance for taking a target photograph of similar quality.

According to the preferred embodiments, an apparatus and method for dynamic routing using dynamic data streams is disclosed. Dynamic routing using dynamic data streams facilitates the creation of a flexible paper gateway in a digital filing system that provides for receiving, processing and storing document images from a wide variety of sources. When thus implemented, dynamic routing allows the digital filing system to efficiently operate while providing digital filing services to a wide variety of users with different needs. Thus, the preferred embodiments provide for the efficient digital filing and efficient management of paper-based information from its receipt at the desktop through an indexing, scanning, image storage and image retrieval process.

The present invention is a diagnosis support system providing automated guidance to a user by automated retrieval of similar disease images and user feedback. High resolution standardized labeled and unlabeled, annotated and non-annotated images of diseased tissue in a database are clustered, preferably with expert feedback. An image retrieval application automatically computes image signatures for a query image and a representative image from each cluster, by segmenting the images into regions and extracting image features in the regions to produce feature vectors, and then comparing the feature vectors using a similarity measure. Preferably the features of the image signatures are extended beyond shape, color and texture of regions, by features specific to the disease. Optionally, the most discriminative features are used in creating the image signatures. A list of the most similar images is returned in response to a query. Keyword query is also supported.

A system and method is provided for handling data in wireless communication devices where data may be captured and linked to a personal journal via indexing and mapping of context data tags abstracted from captured data. The captured data may be retrieved by matching a query to one or more context data tags indexed and mapped to the personal journal. A user preference utilizing one or more of the context data tags linked to the personal journal may facilitate captured data retrieval. The captured data may include multimedia data of an event pre-tagged with indexed information such as user ID, time, date, location and environmental condition or optionally one or more user's biometric data in response to the event. The pre-tagged captured data may be stored in the local host device or transferred to a remote host or storage for later retrieval or post processing.

The present invention is an automated and extensible system for the analysis and retrieval of images based on region-of-interest (ROI) analysis of one or more true objects depicted by an image. The system uses a Regions Of Interest (ROI) database that is a relational or analytical database containing searchable vectors that represent the images stored in a repository. Entries in the ROI database are created by an image locator and ROI classifier that work in tandem to locate images within the repository and extract the relevant information that will be stored in the ROI database. Unlike existing region-of-interest search systems, the ROI classifier analyzes objects in an image to arrive at the actual features of the true object, instead of merely describing the features of the image of that object. Graphical searches are performed by the collaborative workings of an image retrieval module, an image search requestor and an ROI query module. The image search requestor is an abstraction layer that translates user or agent search requests into the language understood by the ROI query.

A system and method for determining image similarity. The method includes the steps of automatically providing perceptually significant features of main subject or background of a first image; automatically providing perceptually significant features of main subject or background of a second image; automatically comparing the perceptually significant features of the main subject or the background of the first image to the main subject or the background of the second image; and providing an output in response thereto. In the illustrative implementation, the features are provided by a number of belief levels, where the number of belief levels are preferably greater than two. The perceptually significant features include color, texture and/or shape. In the preferred embodiment, the main subject is indicated by a continuously valued belief map. The belief values of the main subject are determined by segmenting the image into regions of homogenous color and texture, computing at least one structure feature and at least one semantic feature for each region, and computing a belief value for all the pixels in the region using a Bayes net to combine the features. In an illustrative application, the inventive method is implemented in an image retrieval system. In this implementation, the inventive method automatically stores perceptually significant features of the main subject or background of a plurality of first images in a database to facilitate retrieval of a target image in response to an input or query image. Features corresponding to each of the plurality of stored images are automatically sequentially compared to similar features of the query image. Consequently, the present invention provides an automatic system and method for controlling the feature extraction, representation, and feature-based similarity retrieval strategies of a content-based image archival and retrieval system based on an analysis of main subject and background derived from a continuously valued main subject belief map.

Case images and report text models, each of which is a text model derived from a report text of each of the corresponding case images by making at least certain words/phrases within the report text changeable, are stored in association with each other in a case report storage unit. A case image which is similar to a diagnosis target image is retrieved from the case images stored in the case report storage unit by a similar image retrieval unit. Input of a word/phrase corresponding to the diagnosis target image is accepted in a changeable word/phrase section of the report text model by a report creation unit, thereby a report text of the diagnosis target image is created.

The invention relates to an image retrieval method based on deep learning and Hash. According to the image retrieval method, on the basis of powerful learning capacity of a deep convolutional neural network, deep features of images are extracted, and the problems of weak feature expression capacity and low retrieval precision caused by use of lower features of the images in the prior art are solved; a Hash layer is introduced for construction of a Hash function, learning of the deep features of the images and the construction of the Hash function are completed in the same process, an internal relation of the image features and the Hash function is explored, and the accuracy rate of the image retrieval is greatly increased; quantization error loss is added to a loss layer of the deep convolutional neural network, the expression capacity of Hash codes is enhanced, by means of a Softmax classifier loss module and a quantization error loss module, quantization errors caused by binaryzation in the Hash function are effectively reduced, and the accuracy rate of the image retrieval is further increased.

The invention discloses a multi-task layered image retrieval method based on a depth self-coding convolution neural network. The method is characterized by mainly comprising a multi-task end-to-end convolution neural network for deep learning and training recognition, a rapid visual segmentation detection and positioning method of a region-of-interest secondary screening module based on an RPN network, a coarse search of a full-graph sparse hash code, an area sensing semantic feature and matrix h accurate comparison and search based on the maximum response, and a region-of-interest selectivitycomparison algorithm. According to the method, the end-to-end training can be achieved, the interest region with higher quality can be automatically selected, the automation degree and the intelligent level of search by images can be effectively improved, and the image retrieval requirements of the big data age can be met by using little storage space at a high search speed.

The invention relates to a method for Hash image retrieval based on deep learning and local feature fusion. The method comprises a step (1) of preprocessing an image; a step (2) of using a convolutional neural network to train images containing category tags; a step (3) of using a binarization mode to generate Hash codes of the images and extract 1024-dimensional floating-point type local polymerization vectors; a step (4) of using the Hash codes to perform rough retrieval; and a step (5) of using the local polymerization vectors to perform fine retrieval. According to the method for Hash image retrieval based on deep learning and local feature fusion, an approximate nearest neighbor search strategy is utilized to perform image retrieval after two features are extracted, the retrieval accuracy is high, and the retrieval speed is quick.

An image retrieval system has a network server, at least one client terminal and a data archive of a multiuser file management system, spatially separated from server and client, of a file server. A reduced data stream is now transmitted between server and client and includes a header for transmitting address and meta information with an additional data field that includes an access key to the bulk data stored in the external data archive. This reduces the data volume to be transmitted during image retrieval, and thus reduces the network utilization in the transmission of the data stream occurring between server and client. As a result of this, the time period required to transfer the modified data stream between server and client terminal is correspondingly short.

A method is disclosed for retrieving images relevant to an example image from among a plurality of stored images, each of the stored images being associated with metadata of different types, including retrieving set(s) of images from the stored image(s) for each different type of metadata that are based on similarities of the metadata of each different type with the example image; displaying the retrieved set(s) of image(s) organized according to each different type of metadata; and the user selecting one or more particular set(s) of retrieved image(s).

The invention discloses a deep convolutional neural network end-to-end based image retrieval method by layered deep searching. The method is characterized by mainly including a convolutional neural network used for deep learning, training and recognition, a rapid visual segmentation algorithm for searching image objects, a quick comparison method used for rough searching with a Hash method and Hamming distance fast images, and a precise comparison method for first k ranked images based on images from a candidate pool P. By the method, automation and intelligence level in searching images with images can be effectively heightened, search results can be acquired accurately, and demand of image retrieval in the big data era is satisfied with less storage space and high retrieval speed.

An image retrieval apparatus generates an image feature list having a high and/or a low discrimination capability against other images based on the number of stored image features. Based on the image feature list, the image retrieval apparatus selects feature points extracted from a query image.

To compute a signature for an object comprising or represented by a set of vectors in a vector space of dimensionality D, statistics are computed that are indicative of distribution of the vectors of the set of vectors amongst a set of regions R_i, i=1, . . . , N of the vector space, at least some statistics associated with each region are binarized to generate sets of binary values a_i, i=1, . . . , N indicative of statistics of the vectors of the set of vectors belonging to the respective regions R_i, i=1, . . . , N; and a vector set signature is defined for the set of vectors including the sets of binary values a_i, i=1, . . . , N. The computing, binarizing, and defining operations may be repeated for two sets of vectors, and a quantitative comparison of the two sets of vectors determined based on the corresponding vector set signatures.

A method for searching the world wide web. The method includes receiving one or more keywords for a search and executing a search of the world wide web based on the one or more keywords to identify one or more web pages relevant to one or more keywords. Text versions of the web pages are created by removing images from the web pages. The method includes providing access to the text version of the web pages and displaying the text version of one of the web pages. The method may be used to create tabbed pages for image retrieval. A server and utility interface for implementing the search are also provided.

The invention discloses a modified local sensitive hash vehicle retrieval method based on multitask deep learning. A multitask end-to-end convolution neural network is used to identify a vehicle model, a vehicle system, a vehicle logo, a color and a license plate simultaneously in a subsection parallel mode. A network module for extracting vehicle image example features based on a characteristic pyramid and an algorithm by using a modified local sensitive hash sorting algorithm to sort the vehicle characteristics in a database, and a cross-modal text retrieval method when a retrieval vehicle image can not be acquired are included. The multitask end-to-end convolution neural network and the modified local sensitive hash vehicle retrieval method are provided, the automation level and the intelligence level of vehicle retrieval can be improved effectively, little storage space is used, and image retrieval requirements in a big data era are met by using a quicker retrieval speed.

A depth significance-based remote sensing image rapid retrieval method is disclosed and belongs to the field of computer vision. The method disclosed in the invention specifically relates to technologies such as in-depth learning, significance object detection, image retrieval and the like. According the method, remote sensing images are research objects, and in-depth learning technologies are used for researching a remote sensing image rapid retrieval method. A full convolution neural network is adopted for constructing a multitask significance object detection model which is used for doing significance detection tasks and semantic segmentation tasks at the same time, and depth significance characteristics of the remote sensing images are learnt in network pre-training processes. A depth network structure is improved, a Hash layer fine tuning network is added, and binary system Hash codes of the remote sensing images can be obtained via learning. Significance characteristics and the Hash codes are used comprehensively for similarity measurement. The method disclosed in the invention is of high application value for realizing accurate, highly efficient and feasible retrieval of the remote sensing images.

The present invention discloses a surveillance video pedestrian re-recognition method based on ImageNet retrieval. The pedestrian re-recognition problem is transformed into the retrieval problem of an moving target image database so as to utilize the powerful classification ability of an ImageNet hidden layer feature. The method comprises the steps: preprocessing a surveillance video and removing a large amount of irrelevant static background videos from the video; separating out a moving target from a dynamic video frame by adopting a motion compensation frame difference method and forming a pedestrian image database and an organization index table; carrying out alignment of the size and the brightness on an image in the pedestrian image database and a target pedestrian image; training hidden features of the target pedestrian image and the image in the image database by using an ImageNet deep learning network, and performing image retrieving based on cosine distance similarity; and in a time sequence, converging the relevant videos containing recognition results into a video clip reproducing the pedestrian activity trace. The method disclosed by the present invention can better adapt to changes in lighting, perspective, gesture and scale so as to effective improve accuracy and robustness of a pedestrian recognition result in a camera-cross environment.

The invention discloses a video classification method based on a mixed convolution and attention mechanism, which solves the problems of complex calculation and low accuracy of the prior art. The method comprises the following steps of: selecting a video classification data set; Segmented sampling of input video; Preprocessing two video segments; Constructing hybrid convolution neural network model; The video mixed convolution feature map is obtained in the direction of temporal dimension. Video Attention Feature Map Obtained by Attention Mechanism Operation; Obtaining a video attention descriptor; Training the end-to-end entire video classification model; Test Video to be Categorized. The invention directly obtains mixed convolution characteristic maps for different video segments, Compared with the method of obtaining optical flow features, the method of obtaining optical flow features reduces the computational burden and improves the speed, introduces the attention mechanism betweendifferent video segments, describes the relationship between different video segments and improves the accuracy and robustness, and is used for video retrieval, video tagging, human-computer interaction, behavior recognition, event detection and anomaly detection.

The invention relates to a comprehensive multi-feature image retrieval method, including extraction, index and feature matching of image features, the image features include color feature, texture feature and shape feature. The color feature of the images includes: (1) normalizing the feature as 128 multiply 128 pixel; (2) dividing an image into m multiply n nubs; (3) calculating the C' value of each pixel in every nub, selectiing the main C' value, forming a corresponding two-dimensional matrix A by each main C' value. The invention improves traditional local color histogram by improving extraction method of traditional image color features, which greatly improves precision ratio comparing with common image retrieval method based on color. Application of the image retrieval method that combines multi image features of color, texture and shape can improve precision ratio of the method effectively.