Content-Based Image Retrieval using Deep Learning

A content-based image retrieval (CBIR) system works on the low-level visual features of a user input query image, which makes it difficult for the users to formulate the query and also does not give satisfactory retrieval results. In the past image annotation was proposed as the best possible system for CBIR which works on the principle of automatically assigning keywords to images that help image retrieval users to query images based on these keywords. Image annotation is often regarded as the problem of image classification where the images are represented by some low-level features and the mapping between low-level features and high-level concepts (class labels) is done by some supervised learning algorithms. In a CBIR system learning of effective feature representations and similarity measures is very important for the retrieval performance. Semantic gap has been the key challenge in the past for this problem. A semantic gap exists between low-level image pixels captured by machines and the high-level semantics perceived by humans. Machine learning has been exploited to bridge this gap in the long term. The recent successes of deep learning techniques especially Convolutional Neural Networks (CNN) in solving computer vision applications has inspired me to work on this thesis so as to solve the problem of CBIR using a dataset of annotated images

Adaptive image retrieval using a graph model for semantic feature integration

The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)

The application of user log for online business environment using content-based Image retrieval system

Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers

Semantic bottleneck for computer vision tasks

This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is entirely expressed with natural language , while retaining the efficiency of numerical representations. We show that our approach is able to generate semantic representations that give state-of-the-art results on semantic content-based image retrieval and also perform very well on image classification tasks. Intelligibility is evaluated through user centered experiments for failure detection