With a good image understanding capability, can we manipulate the images high level semantic representation? Such transformation operation can be used to generate or retrieve similar images but with a desired modification (for example changing beach background to street background); similar ability has been demonstrated in zero shot learning, attribute composition and attribute manipulation image search. In this work we show how one can learn transformations with no training examples by learning them on another domain and then transfer to the target domain. This is feasible if: first, transformation training data is more accessible in the other domain and second, both domains share similar semantics such that one can learn transformations in a shared embedding space. We demonstrate this on an image retrieval task where search query is an image, plus an additional transformation specification (for example: search for images similar to this one but background is a street instead of a beach). In one experiment, we transfer transformation from synthesized 2D blobs image to 3D rendered image, and in the other, we transfer from text domain to natural image domain.
: beside traditional text-to-image @cite_16 or image-to-image retrieval @cite_31 task, there are many image retrieval applications with other types of search query such as: sketch @cite_34 , scene layout @cite_13 , relevance feedback @cite_38 @cite_35 , product attribute feedback @cite_14 @cite_5 @cite_8 @cite_6 , dialog interaction @cite_27 and image text combination query @cite_28 . In this work, the image search query will be a combination of a reference image and a transformation specification. In our setup, labeled retrieval examples are not available, hence a standard training procedure like @cite_5 @cite_28 does not work.
@cite_38 @cite_35 @cite_14 @cite_8 @cite_28 @cite_34 @cite_6 @cite_27 @cite_5 @cite_31 @cite_16 @cite_13
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In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image. For example, we may present an image of the Eiffel tower, and ask the system to find images which are visually similar but are modified in small ways, such as being taken at nighttime instead of during the day. To tackle this task, we learn a similarity metric between a target image and a source image plus source text, an embedding and composing function such that target image feature is close to the source image plus text composition feature. We propose a new way to combine image and text using such function that is designed for the retrieval task. We show this outperforms existing approaches on 3 different datasets, namely Fashion-200k, MIT-States and a new synthetic dataset we create based on CLEVR. We also show that our approach can be used to classify input queries, in addition to image retrieval. Starting from a member of an image database designated the "query image," traditional image retrieval techniques, for example, search by visual similarity, allow one to locate additional instances of a target category residing in the database. However, in many cases, the query image or, more generally, the target category, resides only in the mind of the user as a set of subjective visual patterns, psychological impressions, or "mental pictures." Consequently, since image databases available today are often unstructured and lack reliable semantic annotations, it is often not obvious how to initiate a search session; this is the "page zero problem." We propose a new statistical framework based on relevance feedback to locate an instance of a semantic category in an unstructured image database with no semantic annotation. A search session is initiated from a random sample of images. At each retrieval round, the user is asked to select one image from among a set of displayed images-the one that is closest in his opinion to the target class. The matching is then "mental." Performance is measured by the number of iterations necessary to display an image which satisfies the user, at which point standard techniques can be employed to display other instances. Our core contribution is a Bayesian formulation which scales to large databases. The two key components are a response model which accounts for the user's subjective perception of similarity and a display algorithm which seeks to maximize the flow of information. Experiments with real users and two databases of 20,000 and 60,000 images demonstrate the efficiency of the search process. Multimodal Retrieval efficiency can be improved by textual query reformulation.A graph based keyphrase extraction incorporating correlation of terms is proposed.Textual query is expanded with relevant part of narratives and extracted keyphrases.Text and image features are combined using a weightlearning model.The proposed method improves both image and text retrieval efficiency significantly. Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA. While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval. Traditional image retrieval methods require a "query image" to initiate a search for members of an image category. However, when the image database is unstructured, and when the category is semantic and resides only in the mind of the user, there is no obvious way to begin (the "page zero " problem). We propose a new mathematical framework for relevance feedback based on mental matching and starting from a random sample of images. At each iteration the user declares which of several displayed images is closest to his category; performance is measured by the number of iterations necessary to display an instance. Our core contribution is a Bayesian formulation which scales to large databases with no semantic annotation. The two key components are a response model which accounts for the user's subjective perception of similarity and a display algorithm which seeks to maximize the flow of information. Experiments with real users and a database with 20,000 images demonstrate the efficiency of the search process.