Large-scale Content-based Visual Information Retrieval

Rather than restricting search to the use of metadata, content-based information retrieval methods attempt to index, search and browse digital objects by means of signatures or features describing their actual content. Such methods have been intensively studied in the multimedia community to allow managing the massive amount of raw multimedia documents created every day (e.g. video will account to 84% of U.S. internet traffic by 2018). Recent years have consequently witnessed a consistent growth of content-aware and multi-modal search engines deployed on massive multimedia data. Popular multimedia search applications such as Google images, Youtube, Shazam, Tineye or MusicID clearly demonstrated that the first generation of large-scale audio-visual search technologies is now mature enough to be deployed on real-world big data. All these successful applications did greatly benefit from 15 years of research on multimedia analysis and efficient content-based indexing techniques. Yet the maturity reached by the first generation of content-based search engines does not preclude an intensive research activity in the field. There is actually still a lot of hard problems to be solved before we can retrieve any information in images or sounds as easily as we do in text documents. Content-based search methods actually have to reach a finer understanding of the contents as well as a higher semantic level. This requires modeling the raw signals by more and more complex and numerous features, so that the algorithms for analyzing, indexing and searching such features have to evolve accordingly. This thesis describes several of my works related to large-scale content-based information retrieval. The different contributions are presented in a bottom-up fashion reflecting a typical three-tier software architecture of an end-to-end multimedia information retrieval system. The lowest layer is only concerned with managing, indexing and searching large sets of high-dimensional feature vectors, whatever their origin or role in the upper levels (visual or audio features, global or part-based descriptions, low or high semantic level, etc. ). The middle layer rather works at the document level and is in charge of analyzing, indexing and searching collections of documents. It typically extracts and embeds the low-level features, implements the querying mechanisms and post-processes the results returned by the lower layer. The upper layer works at the applicative level and is in charge of providing useful and interactive functionalities to the end-user. It typically implements the front-end of the search application, the crawler and the orchestration of the different indexing and search services

The Parallel Distributed Image Search Engine (ParaDISE)

Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does not always succeed in modelling high-level concepts that a user is looking for. Modern image retrieval techniques consists of multiple steps and aim to retrieve information from large–scale datasets and not only based on global image appearance but local features and if possible in a connection between visual features and text or semantics. This paper presents the Parallel Distributed Image Search Engine (ParaDISE), an image retrieval system that combines visual search with text–based retrieval and that is available as open source and free of charge. The main design concepts of ParaDISE are flexibility, expandability, scalability and interoperability. These concepts constitute the system, able to be used both in real–world applications and as an image retrieval research platform. Apart from the architecture and the implementation of the system, two use cases are described, an application of ParaDISE in retrieval of images from the medical literature and a visual feature evaluation for medical image retrieval. Future steps include the creation of an open source community that will contribute and expand this platform based on the existing parts

Improving Bag-of-visual-Words model with spatial-temporal correlation for video retrieval

Most of the state-of-art approaches to Query-by-Example (QBE) video retrieval are based on the Bag-of-visual-Words (BovW) representation of visual content. It, however, ig- nores the spatial-temporal information, which is important for similarity measurement between videos. Direct incorpo- ration of such information into the video data representa- tion for a large scale data set is computationally expensive in terms of storage and similarity measurement. It is also static regardless of the change of discriminative power of vi- sual words with respect to di↵erent queries. To tackle these limitations, in this paper, we propose to discover Spatial- Temporal Correlations (STC) imposed by the query exam- ple to improve the BovW model for video retrieval. The STC, in terms of spatial proximity and relative motion co- herence between di↵erent visual words, is crucial to identify the discriminative power of the visual words. We develop a novel technique to emphasize the most discriminative visual words for similarity measurement, and incorporate this STC-based approach into the standard inverted index archi- tecture. Our approach is evaluated on the TRECVID2002 and CC WEB VIDEO datasets for two typical QBE video retrieval tasks respectively. The experimental results demon- strate that it substantially improves the BovW model as well as a state of the art method that also utilizes spatial- temporal information for QBE video retrieval

Semantics of video shots for content-based retrieval

Content-based video retrieval research combines expertise from many different areas, such as signal processing, machine learning, pattern recognition, and computer vision. As video extends into both the spatial and the temporal domain, we require techniques for the temporal decomposition of footage so that specific content can be accessed. This content may then be semantically classified - ideally in an automated process - to enable filtering, browsing, and searching. An important aspect that must be considered is that pictorial representation of information may be interpreted differently by individual users because it is less specific than its textual representation. In this thesis, we address several fundamental issues of content-based video retrieval for effective handling of digital footage. Temporal segmentation, the common first step in handling digital video, is the decomposition of video streams into smaller, semantically coherent entities. This is usually performed by detecting the transitions that separate single camera takes. While abrupt transitions - cuts - can be detected relatively well with existing techniques, effective detection of gradual transitions remains difficult. We present our approach to temporal video segmentation, proposing a novel algorithm that evaluates sets of frames using a relatively simple histogram feature. Our technique has been shown to range among the best existing shot segmentation algorithms in large-scale evaluations. The next step is semantic classification of each video segment to generate an index for content-based retrieval in video databases. Machine learning techniques can be applied effectively to classify video content. However, these techniques require manually classified examples for training before automatic classification of unseen content can be carried out. Manually classifying training examples is not trivial because of the implied ambiguity of visual content. We propose an unsupervised learning approach based on latent class modelling in which we obtain multiple judgements per video shot and model the users' response behaviour over a large collection of shots. This technique yields a more generic classification of the visual content. Moreover, it enables the quality assessment of the classification, and maximises the number of training examples by resolving disagreement. We apply this approach to data from a large-scale, collaborative annotation effort and present ways to improve the effectiveness for manual annotation of visual content by better design and specification of the process. Automatic speech recognition techniques along with semantic classification of video content can be used to implement video search using textual queries. This requires the application of text search techniques to video and the combination of different information sources. We explore several text-based query expansion techniques for speech-based video retrieval, and propose a fusion method to improve overall effectiveness. To combine both text and visual search approaches, we explore a fusion technique that combines spoken information and visual information using semantic keywords automatically assigned to the footage based on the visual content. The techniques that we propose help to facilitate effective content-based video retrieval and highlight the importance of considering different user interpretations of visual content. This allows better understanding of video content and a more holistic approach to multimedia retrieval in the future

Spatial and temporal representations for multi-modal visual retrieval

This dissertation studies the problem of finding relevant content within a visual collection according to a specific query by addressing three key modalities: symmetric visual retrieval, asymmetric visual retrieval and cross-modal retrieval, depending on the kind of data to be processed. In symmetric visual retrieval, the query object and the elements in the collection are from the same kind of visual data, i.e. images or videos. Inspired by the human visual perception system, we propose new techniques to estimate visual similarity in image-to-image retrieval datasets based on non-metric functions, improving image retrieval performance on top of state-of-the-art methods. On the other hand, asymmetric visual retrieval is the problem in which queries and elements in the dataset are from different types of visual data. We propose methods to aggregate the temporal information of video segments so that imagevideo comparisons can be computed using similarity functions. When compared in image-to-video retrieval datasets, our algorithms drastically reduce memory storage while maintaining high accuracy rates. Finally, we introduce new solutions for cross-modal retrieval, which is the task in which either the queries or the elements in the collection are non-visual objects. In particular, we study text-image retrieval in the domain of art by introducing new models for semantic art understanding, obtaining results close to human performance. Overall, this thesis advances the state-of-the-art in visual retrieval by presenting novel solutions for some of the key tasks in the field. The contributions derived from this work have potential direct applications in the era of big data, as visual datasets are growing exponentially every day and new techniques for storing, accessing and managing large-scale visual collections are required

Hybrid models for combination of visual and textual features in context-based image retrieval.

Visual Information Retrieval poses a challenge to intelligent information search systems. This is due to the semantic gap, the difference between human perception (information needs) and the machine representation of multimedia objects. Most existing image retrieval systems are monomodal, as they utilize only visual or only textual information about images. The semantic gap can be reduced by improving existing visual representations, making them suitable for a large-scale generic image retrieval. The best up-to-date candidates for a large-scale Content-based Image Retrieval are models based on the Bag of Visual Words framework. Existing approaches, however, produce high dimensional and thus expensive representations for data storage and computation. Because the standard Bag of Visual Words framework disregards the relationships between the histogram bins, the model can be further enhanced by exploiting the correlations between the visual words. Even the improved visual features will find it hard to capture an abstract semantic meaning of some queries, e.g. straight road in the USA. Textual features, on the other hand, would struggle with such queries as church with more than two towers as in many cases the information about the number of towers would be missing. Thus, both visual and textual features represent complementary yet correlated aspects of the same information object, an image. Existing hybrid approaches for the combination of visual and textual features do not take these inherent relationships into account and thus the combinations performance improvement is limited. Visual and textual features can be also combined in the context of relevance feedback. The relevance feedback can help us narrow down and correct the search. The feedback mechanism would produce subsets of visual query and feedback representations as well as subsets of textual query and textual feedback representations. A meaningful feature combination in the context of relevance feedback should take the inherent inter (visual-textual) and intra (visual-visual, textualtextual) relationships into account. In this work, we propose a principled framework for the semantic gap reduction in large scale generic image retrieval. The proposed framework comprises development and enhancement of novel visual features, a hybrid model for the visual and textual features combination, and a hybrid model for the combination of features in the context of relevance feedback, with both fixed and adaptive weighting schemes (importance of a query and its context). Apart from the experimental evaluation of our models, theoretical validations of some interesting discoveries on feature fusion strategies were also performed. The proposed models were incorporated into our prototype system with an interactive user interface