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Automatic Image Annotation
Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations, then techniques were developed using machine translation to try to translate the textual vocabulary with the 'visual vocabulary', or clustered regions known as ''blobs''. Work following these e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature (computer Vision)
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions. More broadly a ''feature'' is any piece of information which is relevant for solving the computational task related to a certain application. This is the same sense as feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multiclass Labeling
In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies. Multiclass classification should not be confused with multi-label classification, where multiple labels are to be predicted for each instance. General strategies The existing multi-class classification techniques can be categorized into (i) transformation to binary (ii) extension from binary and (iii) hierarchical classification. Transformation to binary This section discusses strategies for reducing the problem of multiclass classification to multiple binary classific ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs. It infers a function from ' consisting of a set of ''training examples''. In supervised learning, each example is a ''pair'' consisting of an input object (typically a vector) and a desired output value (also called the ''supervisory signal''). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outline Of Object Recognition
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades. Approaches based on CAD-like object models * Edge detection * Primal sketch * Marr, Mohan and Nevatia * Lowe * Olivier Faugeras Recognition by parts * Generalized cylinders (Thomas Binford) * Geons (Irving Biederman) * Dickinson, Forsyth and Ponce Appearance-based methods * Use example images (called templates or exemplars) of the objects to perform recognition * Objects look different ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Object Detection
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance. Uses It is widely used in computer vision tasks such as image annotation, vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video. Concept Every object class has its own special features that helps in classifying the class – for example all circles are round. Object class detection uses these special features. For example, when looking ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Object Categorization From Image Search
In computer vision, the problem of object categorization from image search is the problem of training a classifier to recognize categories of objects, using only the images retrieved automatically with an Internet search engine. Ideally, automatic image collection would allow classifiers to be trained with nothing but the category names as input. This problem is closely related to that of content-based image retrieval (CBIR), where the goal is to return better image search results rather than training a classifier for image recognition. Traditionally, classifiers are trained using sets of images that are labeled by hand. Collecting such a set of images is often a very time-consuming and laborious process. The use of Internet search engines to automate the process of acquiring large sets of labeled images has been described as a potential way of greatly facilitating computer vision research. Challenges Unrelated images One problem with using Internet image search results as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Content-based Image Retrieval
Content-based image retrieval, also known as query by image content ( QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this surveyContent-based Multimedia Information Retrieval: State of the Art and Challenges' (Original source, 404'''Content-based Multimedia Information Retrieval: State of the Art and Challenges'', Michael Lew, et al., ACM Transactions on Multimedia Computing, Communications, and Applications, pp. 1–19, 2006. for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, sha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Texture (visual Arts)
In the visual arts, texture is the perceived surface quality of a work of art. It is an element of two-dimensional and three-dimensional designs and is distinguished by its perceived visual and physical properties. Use of ''texture'', along with other elements of design, can convey a variety of messages and emotions. Physical texture The physical texture (also known as ''actual texture'' or ''tactile texture'') are the patterns of variations upon a solid surface. These can include -- but are not limited to -- fur, canvas, wood grain, sand, leather, satin, eggshell, matte, or smooth surfaces such as metal or glass. Physical texture differentiates itself from visual texture by having a ''physical quality'' that can be felt by touching the surface of the texture. Specific use of a texture can affect the smoothness that an artwork conveys. For instance, use of ''rough surfaces'' can be visually active, whilst ''smooth surfaces'' can be visually restful. The use of both can give a s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Content-based Image Retrieval
Content-based image retrieval, also known as query by image content ( QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this surveyContent-based Multimedia Information Retrieval: State of the Art and Challenges' (Original source, 404'''Content-based Multimedia Information Retrieval: State of the Art and Challenges'', Michael Lew, et al., ACM Transactions on Multimedia Computing, Communications, and Applications, pp. 1–19, 2006. for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, sha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Translation
Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation), is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. On a basic level, MT performs mechanical substitution of words in one language for words in another, but that alone rarely produces a good translation because recognition of whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalent words in another language, and many words have more than one meaning. Solving this problem with corpus statistical and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies. Current machine translation software often allows for customizat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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