Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

A Universal Model for Content-Based Image Retrieval

Abstract: In this paper a novel approach for generalized image retrieval based on semantic contents is presented. A combination of three feature extraction methods namely color, texture, and edge histogram descriptor. There is a provision to add new features in future for better retrieval efficiency. Any combination of these methods, which is more appropriate for the application, can be used for retrieval. This is provided through User Interface (UI) in the form of relevance feedback. The image properties analyzed in this work are by using computer vision and image processing algorithms. For color the histogram of images are computed, for texture cooccurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce the computational complexity. The entire system was developed using AForge.Imaging (an open source product), MATLAB .NET Builder, C#, and Oracle 10g. The system was tested with Coral Image database containing 1000 natural images and achieved better results. Keywords—Content Based Image Retrieval (CBIR), Cooccurrence matrix, Feature vector, Edge Histogram Descriptor (EHD), Greedy strategy.

Bridging the semanitic gap in image retrieval

Abstract: The emergence of multimedia technology and the rapidly expanding image and video collections on the Internet have attracted significant research efforts in providing tools for effective retrieval and management of visual data. Image retrieval is based on the availability of a representation scheme of image content. Image content descriptors may be visual features such as color, texture, shape, and spatial relationships, or semantic primitives. Conventional information retrieval was based solely on text, and those approaches to textual information retrieval have been transplanted into image retrieval in a variety of ways. However, "a picture is worth a thousand words." Image contents are much more versatile compared with text, and the amount of visual data is already enormous and still expanding very rapidly. Hoping to cope with these special characteristics of visual data, content-based image retrieval methods have been introduced. It has been widely recognized that the family of image retrieval techniques should become an integration of both low-level visual features addressing the more detailed perceptual aspects and high-level semantic features underlying the more general conceptual aspects of visual data. Neither of these two types of features is sufficient to retrieve or manage visual data in an effective or efficient way. Although efforts have been devoted to combining these two aspects of visual data, the gap between them is still a huge barrier in front of researchers. Intuitive and heuristic approaches do not provide us with satisfactory performance. Therefore, there is an urgent need of finding the latent correlation between low-level features and high-level concepts and merging them from a different perspective. How to find this new perspective and bridge the gap between visual features and semantic features has been a major challenge in this research field. This chapter addresses these issues.

Adaptive Content-Based Image Retrieval with Relevance Feedback

Abstract: Retrieval of images, based on similarities between feature vectors of querying image and those from database, is considered The searching procedure was performed through the two basic steps: an objective one, based on the Euclidean distances and a subjective one based on the user's relevance feedback Images recognized from user as the best matched to a query are labeled and used for updating the query feature vector through a RBF (radial basis function) neural network The searching process is repeated from such subjectively refined feature vectors In practice, several iterative steps are sufficient, as confirmed by intensive simulations

Distance measures for color image retrieval

Abstract: We address the issue of image database retrieval based on color using various vector distance metrics. Our system is based on color segmentation where only a few representative color vectors are extracted from each image and used as image indices. These vectors are then used with vector distance measures to determine similarity between a query color and a database image. We test numerous popular vector distance measures in our system and find that directional measures provide the most accurate and perceptually relevant retrievals.

Scars, marks and tattoos (SMT): Soft biometric for suspect and victim identification

Abstract: Scars, marks and tattoos (SMT) are being increasingly used for suspect and victim identification in forensics and law enforcement agencies. Tattoos, in particular, are getting serious attention because of their visual and demographic characteristics as well as their increasing prevalence. However, current tattoo matching procedure requires human-assigned class labels in the ANSI/NIST ITL 1-2000 standard which makes it time consuming and subjective with limited retrieval performance. Further, tattoo images are complex and often contain multiple objects with large intra-class variability, making it very difficult to assign a single category in the ANSI/NIST standard. We describe a content-based image retrieval (CBIR) system for matching and retrieving tattoo images. Based on scale invariant feature transform (SIFT) features extracted from tattoo images and optional accompanying demographical information, our system computes feature-based similarity between the query tattoo image and tattoos in the criminal database. Experimental results on two different tattoo databases show encouraging results.