Semi-supervised image classification based on a multi-feature image query language

The area of Content-Based Image Retrieval (CBIR) deals with a wide range of research disciplines. Being closely related to text retrieval and pattern recognition, the probably most serious issue to be solved is the so-called \semantic gap". Except for very restricted use-cases, machines are not able to recognize the semantic content of digital images as well as humans. This thesis identifies the requirements for a crucial part of CBIR user interfaces, a multimedia-enabled query language. Such a language must be able to capture the user's intentions and translate them into a machine-understandable format. An approach to tackle this translation problem is to express high-level semantics by merging low-level image features. Two related methods are improved for either fast (retrieval) or accurate(categorization) merging. A query language has previously been developed by the author of this thesis. It allows the formation of nested Boolean queries. Each query term may be text- or content-based and the system merges them into a single result set. The language is extensible by arbitrary new feature vector plug-ins and thus use-case independent. This query language should be capable of mapping semantics to features by applying machine learning techniques; this capability is explored. A supervised learning algorithm based on decision trees is used to build category descriptors from a training set. Each resulting \query descriptor" is a feature-based description of a concept which is comprehensible and modifiable. These descriptors could be used as a normal query and return a result set with a high CBIR based precision/recall of the desired category. Additionally, a method for normalizing the similarity profiles of feature vectors has been developed which is essential to perform categorization tasks. To prove the capabilities of such queries, the outcome of a semi-supervised training session with \leave-one-object-out" cross validation is compared to a reference system. Recent work indicates that the discriminative power of the query-based descriptors is similar and is likely to be improved further by implementing more recent feature vectors.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Investigation in Applying Image Retrieval Techniques to X-Ray Engineering Pictures

Using image retrieval techniques in analysing Non-destructive testing reults is a new challenge in both computing science and engineering applications. Objective of this research is to develop an image retrieval system to analyse X-ray images for welding industry. The content based image retrieval has been used in this investigation, particularly in feature vector paradigm and similarity as well as detailed analysis towards single defects. It is found that X-ray images can be digitally analysed qualitatively and quantitatively easily. It concludes that the use of existing CBIR techniques can provide a platform to quickly develop new image analysis tools

An Extensible Query Language for Content Based Image Retrieval

One of the most important bits of every search engine is the query interface. Complex interfaces may cause users to struggle in learning the handling. An example is the query language SQL. It is really powerful, but usually remains hidden to the common user. On the other hand the usage of current languages for Internet search engines is very simple and straightforward. Even beginners are able to find relevant documents. This paper presents a hybrid query language suitable for both image and text retrieval. It is very similar to those of a full text search engine but also includes some extensions required for content based image retrieval. The language is extensible to cover arbitrary feature vectors and handle fuzzy queries

Semi-Supervised Image Classification based on a Multi-Feature Image Query Language

The area of Content-Based Image Retrieval (CBIR) deals with a wide range of research disciplines. Being closely related to text retrieval and pattern recognition, the probably most serious issue to be solved is the so-called \semantic gap". Except for very restricted use-cases, machines are not able to recognize the semantic content of digital images as well as humans. This thesis identifies the requirements for a crucial part of CBIR user interfaces, a multimedia-enabled query language. Such a language must be able to capture the user's intentions and translate them into a machine-understandable format. An approach to tackle this translation problem is to express high-level semantics by merging low-level image features. Two related methods are improved for either fast (retrieval) or accurate(categorization) merging. A query language has previously been developed by the author of this thesis. It allows the formation of nested Boolean queries. Each query term may be text- or content-based and the system merges them into a single result set. The language is extensible by arbitrary new feature vector plug-ins and thus use-case independent. This query language should be capable of mapping semantics to features by applying machine learning techniques; this capability is explored. A supervised learning algorithm based on decision trees is used to build category descriptors from a training set. Each resulting \query descriptor" is a feature-based description of a concept which is comprehensible and modifiable. These descriptors could be used as a normal query and return a result set with a high CBIR based precision/recall of the desired category. Additionally, a method for normalizing the similarity profiles of feature vectors has been developed which is essential to perform categorization tasks. To prove the capabilities of such queries, the outcome of a semi-supervised training session with \leave-one-object-out" cross validation is compared to a reference system. Recent work indicates that the discriminative power of the query-based descriptors is similar and is likely to be improved further by implementing more recent feature vectors

An extensible query language for content based image retrieval based on Lucene

One of the most important bits of every search engine is the query interface. Complex interfaces may cause users to struggle in learning the handling. An example is the query language SQL. It is really powerful, but usually remains hidden to the common user. On the other hand the usage of current languages for Internet search engines is very simple and straightforward. Even beginners are able to find relevant documents. This paper presents a hybrid query language suitable for both image and text retrieval. It is very similar to those of a full text search engine but also includes some extensions required for content based image retrieval. The language is extensible to cover arbitrary feature vectors and handle fuzzy querie

Integrating Mobile Computing Solutions into Distance Learning Environments

This paper assumes that in the near future most students at universities are in possession of a mobile device with the ability to access the world wide web and display multimedia content. Observing the current development trends of mobile hardware, this seems to be justified. Even common mobile phones have multiple wireless communication interfaces and colour displays. More expensive devices are equipped with a large touch screen and are able to play videos in a reasonable quality. It is attempted to exploit this development and enhance the learning experience of students by gradually building up a pervasive computing infrastructure. A design is proposed that offers an open and extensible (distance) learning environment. Flexible standards such as the Hypertext Transfer Protocol (HTTP) for communication and the Extensible Markup Language (XML) for document transfer are used. This design allows to access and modify learning material stored in learning management systems (LMS), multimedia repositories and electronic voting system (EVS) locally and remotely. The supported technology ranges from PCs and laptops to mobile devices

An Investigation in Image Retrieval for Analysing Welding Defects

The development of new approaches in image processing and retrieval provides several opportunities in supporting in different domains. The group of welding engineers frequently needs to conduct visual inspections to assess the quality of weldings. It is investigated, if this process can be supported by different kinds of software. A generic CBIR system has been successfully used to sort welding photographs according to the severity of visual faults. Similar algorithms were used to automatically spot and measure the diameter of gas pores

Using CBIR and semantics in 3D-model retrieval

This paper proposes a generic design for 3D model retrieval. It has been developed in cooperation with EADS, which deals with Computer Aided Design / Computer Aided Engineering (below CAD/CAE) data through the product life cycle. Sharing these models with users across the enterprise is a challenging task. CAD/CAE models may be hundreds of megabytes large and stored in proprietary formats. Browsing and previewing this data efficiently requires new tools. One way to leverage the collaboration in and outside of CAD domains is to offer through a repository an access to a neutral 3D data format. Also functionalities for semantic enrichment of 3D models, for the retrieval of context sensitive information and for 3D model retrieval based on 3D similarity search and CBIR techniques should be provided

Content based image retrieval by combining features and query-by-sketch

This paper reports an approach to improve content-based image retrieval systems. Most current systems are based on a single technique for feature extraction and similarity search. Each technique has its advantages and drawbacks concerning the result quality. Usually they cover one or two certain features of the image, e.g. histograms or shape information. To overcome these restrictions a flexible framework is proposed, capable of combining several different features in a single retrieval system. This system allows an administrator to build a repository managing different feature vectors. A user searching through this repository defines and weights these features according to his needs in the query. It concludes that a combined retrieval can be used much more widely than a highly specialized one and the use of query-by-sketch or -example combined with semantic information (e.g. keywords) could enhance the result quality

An Intelligent System for Analyzing Welding Defects using Image Retrieval Techniques

The development of new approaches in image processing and retrieval provides several opportunities in supporting in different domains. The group of welding engineers frequently needs to conduct visual inspections to assess the quality of welding products. It is investigated, if this process can be supported by different kinds of software. Techniques from a generic CBIR system have been successfully used to cluster welding photographs according to the severeness of visual faults. Similarity algorithms were used to automatically spot faults, such as cracks and gas pores