image search by concept map

8/6/2019 Image Search by Concept Map

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Hao Xu Jingdong Wang Xian-Sheng Hua Shipeng Li

University of Science and Technology of China,

SIGIR '10

Filiberto Vilca ApazaStudent of the School of Computer Science

San Pablo Catholic University, Arequipa, [email protected]

30 May 2011


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1. Introduction.

2. Overview.3. Approach.

Query Formulation

Concept Map Translation.

4. Experiments.

Setting.

Quantitative Search Performance.

Visual Results.

User Study.

5. Conclusion.

Image Search By Concept Map. 1


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Digital Image is nowadays the second most prevalent media in theweb.

Variety of search interfaces.(Examples). Researchs about search interface design.

In this Paper :

1) They present a novel image search system to enable users to search imageswith the requirement on the spatial distribution of semantic concepts.*

2) They propose a novel image search interface to enable users to intuitivelyinput a so-called concept map to indicate what concepts should be included and

where they should appear.

3) They introduce an effective technique to translate the concept map into visualinstance map.



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Figure 2: User interface of the proposed system.


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Query Formulation.

Concept Map Translation.



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It only requieres the user to simply select a position and type textualkeywords.

The user can express the spatial intention in a visual way.

The user may delete or edit an existing keyword or modify the

position of a concept by dragging the corresponding keyword.

The user may also care about the sizes of the concepts, the sizes ofthe regions occupied by the concepts

A rectangle is associated with each keyword to indicate the influencescope of the keyword.



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Figure 2. The flowchart of concept map translation.


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Text-based image search engines are successful if the textual queryis relatively simple.

First they collect a set of images to search in a text-based imagesearch engine with the keyword then adopt the Affinity Propagation

algorithm( AP) to find the visual instance,

Run AP only for the top ranked images returned by the text-basedimage search engine

Sort the exemplars in a descending order of the sizes of theirassociated image groups.



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There Goal here is to estimate the spatial intention and represent itby a group of spatial distributions each of which corresponds to a

concept. The spatial intention estimation follows two principles:

1) A concept has larger probability to

appear near the specified position.

2) A concept is not expected to appear where another concept should appear.



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They denote a concept map as :

The spatial distribution of concept k is:



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To evaluate the proposed system, we design 42 image search taskswith the requirement on the concept layout.


Figure 4: Examples of the image search tasks in the experiment.


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The basic idea of this method is to pick an example image from thesearch results of the text-based image search system (initial searchresults). To accomplish a given task, we collect the most relevant

images and use them one at a time as the example image to perform"show similar image" search.


Figure 5: (a) Quantitative comparison of the image search performance for the threemethods.

(b) Illustration of the influence of the number of visual instances V

(c) Illustration of the effectiveness of the advanced function influence scope selection.


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They have presented a novel image search system to enable users to

search images with the particular requirement on the concept layout.

A new formulation of image search query called concept map isintroduced so that the user can explicitly indicate not only the desiredconcepts but also the expected layout of the concepts in the images.

The experiments demostrate that the proposed system is effective tohandle the particular kind of image search tasks.


image search by concept map

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