What is content based image and video 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 survey for a scientific …
What is content based information retrieval?
A content-based retrieval system processes the information contained in image data and creates an abstraction of its content in terms of visual attributes. Any query operations deal solely with this abstraction rather than with the image itself.
What is image retrieval techniques?
Image retrieval  is a computer technique for browsing, searching and retrieving images from a large database of digital images. Originally, TBIR techniques are using some keywords of the images to retrieve the target images. It is a manually image annotation technique.
What is texture in CBIR?
The texture feature is another wildly used feature in CBIR, which intended to capture the granularity and repetitive patterns of surfaces within an image .
What is video indexing and retrieval?
Video data indexing and retrieval which applies tags to large video databases, is useful as a complementary means for applications which are having multi media content and need faster search responses. Well-ordered and effective management of video documents depends on the availability of indexes.
Why is image retrieval important?
In the last few years, the fast growth of the Internet has largely increased the number of image collections available. The accumulation of these image collections is attracting more and more users in various professional fields [Rui Y et al., 1999].
What is visual information retrieval?
Visual information retrieval (VIR) is an interdisciplinary field of research whose techniques aim to solve the problem of finding relevant (documents containing) images and videos based on a query.
What is video retrieval system?
Content Based Video Retrieval (CBVR) has been increasingly used to describe the process of retrieving desired videos from a large collection on the basis of features that are extracted from the videos.
What is content based image retrieval and how does it work?
CBIR is the process by which one searches for similar images according to the content of the query image, such as color, texture, shape, and so forth. Learn more in: Probability Association Approach in Automatic Image Annotation.
How do you determine the color of a histogram?
In general, a color histogram is based on a certain color space, such as RGB or HSV. When we compute the pixels of different colors in an image, if the color space is large, then we can first divide the color space into certain numbers of small intervals. Each of the intervals is called a bin.
What is the necessity of video indexing?
What is image content?
Image content search is the capacity for software to recognize objects in digital images and return a search engine results page (SERP) based on a user query.
What is similarity based retrieval?
Similarity-based image retrieval, which has become an important area of computer vision, is a part of the case-based reasoning scenario. In similarity-based retrieval, a query image is provided and similar images from a database are retrieved, usually in order of similarity.
What are the various retrieval systems and queries in content-based retrieval systems in multimedia database?
There is many query types for multi-key based retrieval: exact match, partial match, range, and partial range. In content-based retrieval systems, the collections of multimedia objects are stored as digitized representations. The queries are characterized by fuzziness and ambiguity.
How video data is stored and retrieved?
Videos are generally stored in the MPEG format which is a compressed domain representation. The use of a large number of exisiting techniques for compressed domain, avoids explicit decoding of video, and can convey further information without visual recognition and understanding.
What is image retrieval deep learning?
Content based image retrieval (CBIR) systems enable to find similar images to a query image among an image dataset. The most famous CBIR system is the search per image feature of Google search. This article uses the keras deep learning framework to perform image retrieval on the MNIST dataset.
What is color histogram feature extraction?
Methods for color feature extraction Color Histogram is the most widely used technique for extracting the color feature of an image [2, 3]. It represents the image from a different perspective. It represents the frequency distribution of color bins in an image. It counts similar pixels and store it.
What are color histograms used for?
A color histogram of an image represents the distribution of the composition of colors in the image. It shows different types of colors appeared and the number of pixels in each type of the colors appeared.
What does indexing a video mean?
Depending on the purpose, indexing identifies the location of resources based on file names, key data fields in a database record, text within a file or unique attributes in a graphics or video file.
Why are images important in content?
Visuals don’t just stand out at first sight; they’re also easier to remember. Add visuals to well-researched and useful content and that content is much more likely to resonate with its audience over time. Visual aids can improve learning by up to 400 percent and be processed 60,000 times faster than text alone.
What is color based image retrieval?
Color based image retrieval is the most basic and most important method for CBIR. Color features are the most intuitive and most obvious image features. It is also an important feature of perception. Comparing with other image features such as texture and shape etc., color features are very stable and robust.
What features are fused to realize comprehensive image retrieval?
Color feature and texture feature are fused to realize comprehensive image retrieval. The weight of color and texture features is determined through lots of experiments. Linear weighted mode combining with similar distances of color and texture features are adopted to retrieve images comprehensively.
How do you extract the dominant color of an image?
Step 1. The HSV color space is selected, which can reproduce human vision color features well. Under the color space, the dominant color of the initial image is extracted as color feature vectors through a clustering–analyzing algorithm considering the color resolution and color feature dimensions. Step 2.
What are features in image processing?
Features are the basis for CBIR, which are certain visual properties of an image. The features are either global for the entire image or local for a small group of pixels. According to the methods used for CBIR, features can be classified into low-level features and high-level features.