Visual Information Retrieval in Digital Libraries

The emergence of information highways and multimedia computing has resulted in redefining the concept of libraries. It is widely believed that in the next few years, a significant portion of information in libraries will be in the form of multimedia electronic documents. Many approaches are being proposed for storing, retrieving, assimilating, harvesting, and prospecting information from these multimedia documents. Digital libraries are expected to allow users to access information independent of the locations and types of data sources and will provide a unified picture of information. In this paper, we discuss requirements of these emerging information systems and present query methods and data models for these systems. Finally, we briefly present a few examples of approaches that provide a preview of how things will be done in the digital libraries in the near future.published or submitted for publicatio

Semantic Image Retrieval via Active Grounding of Visual Situations

We describe a novel architecture for semantic image retrieval---in particular, retrieval of instances of visual situations. Visual situations are concepts such as "a boxing match," "walking the dog," "a crowd waiting for a bus," or "a game of ping-pong," whose instantiations in images are linked more by their common spatial and semantic structure than by low-level visual similarity. Given a query situation description, our architecture---called Situate---learns models capturing the visual features of expected objects as well the expected spatial configuration of relationships among objects. Given a new image, Situate uses these models in an attempt to ground (i.e., to create a bounding box locating) each expected component of the situation in the image via an active search procedure. Situate uses the resulting grounding to compute a score indicating the degree to which the new image is judged to contain an instance of the situation. Such scores can be used to rank images in a collection as part of a retrieval system. In the preliminary study described here, we demonstrate the promise of this system by comparing Situate's performance with that of two baseline methods, as well as with a related semantic image-retrieval system based on "scene graphs.

CROEQS: Contemporaneous Role Ontology-based Expanded Query Search: implementation and evaluation

Searching annotated items in multimedia databases becomes increasingly important. The traditional approach is to build a search engine based on textual metadata. However, in manually annotated multimedia databases, the conceptual level of what is searched for might differ from the high-levelness of the annotations of the items. To address this problem, we present CROEQS, a semantically enhanced search engine. It allows the user to query the annotated persons not only on their name, but also on their roles at the time the multimedia item was broadcast. We also present the ontology used to expand such queries: it allows us to semantically represent the domain knowledge on people fulfilling a role during a temporal interval in general, and politicians holding a political office specifically. The evaluation results show that query expansion using data retrieved from an ontology considerably filters the result set, although there is a performance penalty

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Sparse Transfer Learning for Interactive Video Search Reranking

Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. In this paper, we adopt interactive video search reranking to bridge the semantic gap by introducing user's labeling effort. We propose a novel dimension reduction tool, termed sparse transfer learning (STL), to effectively and efficiently encode user's labeling information. STL is particularly designed for interactive video search reranking. Technically, it a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, b) achieves a sparse representation for the subspace to encodes user's intention by applying the elastic net penalty, and c) propagates user's labeling information from labeled samples to unlabeled samples by using the data distribution knowledge. We conducted extensive experiments on the TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular dimension reduction algorithms. We report superior performance by using the proposed STL based interactive video search reranking.Comment: 17 page

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

In this paper, we present a label transfer model from texts to images for image classification tasks. The problem of image classification is often much more challenging than text classification. On one hand, labeled text data is more widely available than the labeled images for classification tasks. On the other hand, text data tends to have natural semantic interpretability, and they are often more directly related to class labels. On the contrary, the image features are not directly related to concepts inherent in class labels. One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images. This is implemented by learning a transfer function as a bridge to propagate the labels between two multimodal spaces. However, the intermodal label transfers could be undermined by blindly transferring the labels of noisy texts to annotate images. To mitigate this problem, we present an intramodal label transfer process, which complements the intermodal label transfer by transferring the image labels instead when relevant text is absent from the source corpus. In addition, we generalize the inter-modal label transfer to zero-shot learning scenario where there are only text examples available to label unseen classes of images without any positive image examples. We evaluate our algorithm on an image classification task and show the effectiveness with respect to the other compared algorithms.Comment: The paper has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence. It will apear in a future issu