Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

Active SVM-based Relevance Feedback with Hybrid Visual and representation

Most of the available image databases have keyword annotations associated with the images, related to the image context or to the semantic interpretation of image content. Keywords and visual features provide complementary information, so using these sources of information together is an advantage in many applications. We address here the challenge of semantic gap reduction, through an active SVM-based relevance feedback method, jointly with a hybrid visual and conceptual content representation and retrieval. We first introduce a new feature vector, based on the keyword annotations available for the images, which makes use of conceptual information extracted from an external ontology and represented by ``core concepts''. We then present two improvements of the SVM-based relevance feedback mechanism: a new active learning selection criterion and the use of specific kernel functions that reduce the sensitivity of the SVM to scale. We evaluate the use of the proposed hybrid feature vector composed of keyword representations and the low level visual features in our SVM-based relevance feedback setting. Experiments show that the use of the keyword-based feature vectors provides a significant improvement in the quality of the results

Can a workspace help to overcome the query formulation problem in image retrieval?

We have proposed a novel image retrieval system that incorporates a workspace where users can organise their search results. A task-oriented and user-centred experiment has been devised involving design professionals and several types of realistic search tasks. We study the workspace’s effect on two aspects: task conceptualisation and query formulation. A traditional relevance feedback system serves as baseline. The results of this study show that the workspace is more useful with respect to both of the above aspects. The proposed approach leads to a more effective and enjoyable search experience

Exploring EEG for Object Detection and Retrieval

This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based image retrieval. We investigate if it is possible to capture useful EEG signals to detect if relevant objects are present in a dataset of realistic and complex images. We perform several experiments using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We then use the feedback from the BCI and mouse-based interfaces to retrieve localized objects in a subset of TRECVid images. We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others. When the users have limited time to annotate the images (100 seconds in our experiments) both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback outperforms mouse-based feedback. The realistic and complex image dataset differentiates our work from previous studies on EEG for image retrieval.Comment: This preprint is the full version of a short paper accepted in the ACM International Conference on Multimedia Retrieval (ICMR) 2015 (Shanghai, China

Using segmented objects in ostensive video shot retrieval

This paper presents a system for video shot retrieval in which shots are retrieved based on matching video objects using a combination of colour, shape and texture. Rather than matching on individual objects, our system supports sets of query objects which in total reflect the user’s object-based information need. Our work also adapts to a shifting user information need by initiating the partitioning of a user’s search into two or more distinct search threads, which can be followed by the user in sequence. This is an automatic process which maps neatly to the ostensive model for information retrieval in that it allows a user to place a virtual checkpoint on their search, explore one thread or aspect of their information need and then return to that checkpoint to then explore an alternative thread. Our system is fully functional and operational and in this paper we illustrate several design decisions we have made in building it

TRECVID 2004 experiments in Dublin City University

In this paper, we describe our experiments for TRECVID 2004 for the Search task. In the interactive search task, we developed two versions of a video search/browse system based on the Físchlár Digital Video System: one with text- and image-based searching (System A); the other with only image (System B). These two systems produced eight interactive runs. In addition we submitted ten fully automatic supplemental runs and two manual runs. A.1, Submitted Runs: • DCUTREC13a_{1,3,5,7} for System A, four interactive runs based on text and image evidence. • DCUTREC13b_{2,4,6,8} for System B, also four interactive runs based on image evidence alone. • DCUTV2004_9, a manual run based on filtering faces from an underlying text search engine for certain queries. • DCUTV2004_10, a manual run based on manually generated queries processed automatically. • DCU_AUTOLM{1,2,3,4,5,6,7}, seven fully automatic runs based on language models operating over ASR text transcripts and visual features. • DCUauto_{01,02,03}, three fully automatic runs based on exploring the benefits of multiple sources of text evidence and automatic query expansion. A.2, In the interactive experiment it was confirmed that text and image based retrieval outperforms an image-only system. In the fully automatic runs, DCUauto_{01,02,03}, it was found that integrating ASR, CC and OCR text into the text ranking outperforms using ASR text alone. Furthermore, applying automatic query expansion to the initial results of ASR, CC, OCR text further increases performance (MAP), though not at high rank positions. For the language model-based fully automatic runs, DCU_AUTOLM{1,2,3,4,5,6,7}, we found that interpolated language models perform marginally better than other tested language models and that combining image and textual (ASR) evidence was found to marginally increase performance (MAP) over textual models alone. For our two manual runs we found that employing a face filter disimproved MAP when compared to employing textual evidence alone and that manually generated textual queries improved MAP over fully automatic runs, though the improvement was marginal. A.3, Our conclusions from our fully automatic text based runs suggest that integrating ASR, CC and OCR text into the retrieval mechanism boost retrieval performance over ASR alone. In addition, a text-only Language Modelling approach such as DCU_AUTOLM1 will outperform our best conventional text search system. From our interactive runs we conclude that textual evidence is an important lever for locating relevant content quickly, but that image evidence, if used by experienced users can aid retrieval performance. A.4, We learned that incorporating multiple text sources improves over ASR alone and that an LM approach which integrates shot text, neighbouring shots and entire video contents provides even better retrieval performance. These findings will influence how we integrate textual evidence into future Video IR systems. It was also found that a system based on image evidence alone can perform reasonably and given good query images can aid retrieval performance

Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

Evaluating a workspace's usefulness for image retrieval

Image searching is a creative process. We have proposed a novel image retrieval system that supports creative search sessions by allowing the user to organise their search results on a workspace. The workspace’s usefulness is evaluated in a task-oriented and user-centred comparative experiment, involving design professionals and several types of realistic search tasks. In particular, we focus on its effect on task conceptualisation and query formulation. A traditional relevance feedback system serves as a baseline. The results of this study show that the workspace is more useful in terms of both of the above aspects and that the proposed approach leads to a more effective and enjoyable search experience. This paper also highlights the influence of tasks on the users’ search and organisation strategy

Dublin City University video track experiments for TREC 2003

In this paper, we describe our experiments for both the News Story Segmentation task and Interactive Search task for TRECVID 2003. Our News Story Segmentation task involved the use of a Support Vector Machine (SVM) to combine evidence from audio-visual analysis tools in order to generate a listing of news stories from a given news programme. Our Search task experiment compared a video retrieval system based on text, image and relevance feedback with a text-only video retrieval system in order to identify which was more effective. In order to do so we developed two variations of our Físchlár video retrieval system and conducted user testing in a controlled lab environment. In this paper we outline our work on both of these two tasks