Content Based Image Recognition
Content based image retrieval is a difficult philosophy of catching pertinent pictures from a huge space for putting away. Despite the fact that this zone has been investigated for a considerable length of time, no method has accomplished the accuracy of human visual discernment in distinguishing pictures.
Project Description
Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values.
The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphone, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem. The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic relationship with the query given by the user. Most of the search engines on the Internet retrieve the images on the basis of text-based approaches that require captions as input .
The user submits a query by entering some text or keywords that are matched with the keywords that are placed in the archive. The output is generated on the basis of matching in keywords, and this process can retrieve the images that are not relevant. The difference in human visual perception and manual labeling/annotation is the main reason for generating the output that is irrelevant . It is near to impossible to apply the concept of manual labeling to existing large size image archives that contain millions of images. The second approach for image retrieval and analysis is to apply an automatic image annotation system that can label image on the basis of image contents. The approaches based on automatic image annotation are dependent on how accurate a system is in detecting color, edges, texture, spatial layout, and shape-related information .