A graph theory-based online keywords model for image semantic extraction

Image captions and keywords are the semantic descriptions of the dominant visual content features in a targeted visual scene. Traditional image keywords extraction processes involves intensive data- and knowledge-level operations by using computer vision and machine learning techniques. However, recent studies have shown that the gap between pixel-level processing and the semantic definition of an image is difficult to bridge by counting only the visual features. In this paper, augmented image semantic information has been introduced through harnessing functions of online image search engines. A graphical model named as the “Head-words Relationship Network” (HWRN) has been devised for tackling the aforementioned problems. The proposed algorithm starts from retrieving online images of similarly visual features from the input image, the text content of their hosting webpages are then extracted, classified and analysed for semantic clues. The relationships of those “head-words” from relevant webpages can then be modelled and quantified using linguistic tools. Experiments on the prototype system have proven the effectiveness of this novel approach. Performance evaluation over benchmarking state-of-the-art approaches has also shown satisfactory results and promising future applications

ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

In today�s world, online shopping is very attractive and grown exponentially due to revolution in digitization. It is a crucial demand to provide recommendation for all the search engine to identify users� need. In this paper, we have proposed a ANOVA Cosine Similarity Image Recommendation (ACSIR) framework for vertical image search where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p value by considering image visual features on text-based search. Expanded queries are generated for user input query, and text-based search is performed to get the initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain-specific site. Experiment results show that the ACSIR outperforms iLike method by providing more relevant products to the user input query. © 2017, Springer-Verlag London

A Two-View Learning Approach for Image Tag Ranking

Singapore Ministry of Education Academic Research Fund Tier

Image Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images

Parallelizing support vector machines for scalable image annotation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced. The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers. The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

Aplicación del modelo Bag-of-Words al reconocimiento de imágenes

Object recognition on images has been more investigated in the recent years. Its principal application is the image retrieval and, therefore, image searchers would find the solution to the query based on whether the image has certain objects in its visual content or not instead of based on the adjacent textual annotations. Content based image retrieval would improve notoriously the quality of searchers. It is neccesary to have models that classify an image based on its low level features. In this project, it is used the ‘Bag of words’ model. Multimedia information retrieval entails many fields involved, and has many applications. The objective of this project is the indexing of images of a database based on content. It tries to eliminate the semantic gap finding the descriptors of each imagen, and therefore decide to which class or which semantic concept belongs.--------------------------------------------------------------------El reconocimiento de objetos en imágenes es un campo cada vez más investigado y que se aplica principalmente a la recuperación de imágenes basada en contenido, es decir, a buscadores de imágenes que encontrarán la solución a una consulta basándose en si la imagen contiene ciertos objetos o no en función de su contenido visual, y no de las anotaciones textuales colindantes. Su aplicación surge de la necesidad de sistemas de gestión automatizada de documentos multimedia que sustituyan a la gestión manual, ya que ciertas bases de datos de información multimedia tienen tamaños impracticables para realizar una anotación manual. La recuperación de imágenes basada en contenido mejoraría significativamente la calidad de las búsquedas. Para ello es necesario disponer de modelos que se enfrenten a la clasificación de una imagen a partir de sus características de bajo nivel. En este proyecto se va a utilizar el modelo Bag-of-words (BoW). La recuperación de información multimedia conlleva muchos campos involucrados: clasificadores de información, estadísticas de señales, visión artificial… Por otro lado, también tiene multitud de aplicaciones: buscadores Web, detección de rostros en fotografías, recuperación de imágenes médicas, robótica, etc. Este proyecto tiene como objetivo la indexación de las imágenes de una base de datos basándose en el contenido. Trata de eliminar la laguna semántica hallando los descriptores de cada imagen de la base de datos para luego discernir a qué clase o concepto semántico pertenecen.Ingeniería Técnica en Sonido e Image

Parallelizing support vector machines for scalable image annotation

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced. The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers. The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications.EThOS - Electronic Theses Online ServiceGBUnited Kingdo