Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.
* Exploiting Image Search: The idea of exploiting web image search is adopted in many tasks, including object classification @cite_21 and video summarization @cite_25 . These approaches collect a vast amount of images from the web and utilize them to extract canonical visual concepts. Recent label prediction for images by Johnson al @cite_19 infers tags of target images by mining relevant Flickr images based on their metadata, such as user tags and photo groups curated by users. The relevant images serve as priors on tags for the target image. A similar motivation drives us to utilize web images for each sentence, which can disambiguate visual concepts of the sentence and highlight relevant target videos.
@cite_19 @cite_21 @cite_25
2080289064 385190221 2029163572 2070753207
The objective of this work is to automatically generate a large number of images for a specified object class (for example, penguin). A multi-modal approach employing both text, meta data and visual features is used to gather many, high-quality images from the Web. Candidate images are obtained by a text based Web search querying on the object identifier (the word penguin). The Web pages and the images they contain are downloaded. The task is then to remove irrelevant images and re-rank the remainder. First, the images are re-ranked using a Bayes posterior estimator trained on the text surrounding the image and meta data features (such as the image alternative tag, image title tag, and image filename). No visual information is used at this stage. Second, the top-ranked images are used as (noisy) training data and a SVM visual classifier is learnt to improve the ranking further. The principal novelty is in combining text meta-data and visual features in order to achieve a completely automatic ranking of the images. Examples are given for a selection of animals (e.g. camels, sharks, penguins), vehicles (cars, airplanes, bikes) and other classes (guitar, wristwatch), totalling 18 classes. The results are assessed by precision recall curves on ground truth annotated data and by comparison to previous approaches including those of (on an additional six classes) and Relevant and irrelevant web images collected by tag-based image retrieval have been employed as loosely labeled training data for learning SVM classifiers for image categorization by only using the visual features. In this work, we propose a new image categorization method by incorporating the textual features extracted from the surrounding textual descriptions (tags, captions, categories, etc.) as privileged information and simultaneously coping with noise in the loose labels of training web images. When the training and test samples come from different datasets, our proposed method can be further extended to reduce the data distribution mismatch by adding a regularizer based on the Maximum Mean Discrepancy (MMD) criterion. Our comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed methods for image categorization and image retrieval by exploiting privileged information from web data. We introduce an approach to image retrieval and auto-tagging that leverages the implicit information about object importance conveyed by the list of keyword tags a person supplies for an image. We propose an unsupervised learning procedure based on Kernel Canonical Correlation Analysis that discovers the relationship between how humans tag images (e.g., the order in which words are mentioned) and the relative importance of objects and their layout in the scene. Using this discovered connection, we show how to boost accuracy for novel queries, such that the search results better preserve the aspects a human may find most worth mentioning. We evaluate our approach on three datasets using either keyword tags or natural language descriptions, and quantify results with both ground truth parameters as well as direct tests with human subjects. Our results show clear improvements over approaches that either rely on image features alone, or that use words and image features but ignore the implied importance cues. Overall, our work provides a novel way to incorporate high-level human perception of scenes into visual representations for enhanced image search. This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as image-to-image search, tag-to-image search, and image-to-tag search (image annotation). We start with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporate a third view capturing high-level image semantics, represented either by a single category or multiple non-mutually-exclusive concepts. We present two ways to train the three-view embedding: supervised, with the third view coming from ground-truth labels or search keywords; and unsupervised, with semantic themes automatically obtained by clustering the tags. To ensure high accuracy for retrieval tasks while keeping the learning process scalable, we combine multiple strong visual features and use explicit nonlinear kernel mappings to efficiently approximate kernel CCA. To perform retrieval, we use a specially designed similarity function in the embedded space, which substantially outperforms the Euclidean distance. The resulting system produces compelling qualitative results and outperforms a number of two-view baselines on retrieval tasks on three large-scale Internet image datasets.