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Spatial Colour Matching for Content Based Retrieval and Navigation

Spatial Colour Matching for Content Based Retrieval and Navigation
Spatial Colour Matching for Content Based Retrieval and Navigation

Spatial Colour Matching for Content Based Retrieval and

Navigation

David Dupplaw,Paul Lewis,Mark Dobie

Multimedia Research Group,University of Southampton,

Southampton,Hampshire,England

Abstract

In this paper we describe two approaches for providing a versatile mechanism for retrieval and navigation using

spatially related feature selections.As an example,we build colour histograms from images,and use a quadratic

histogram match to achieve a retrieval method based on the spatial colour distribution of the images.Navigation is

facilitated by a larger architecture,called MA VIS(Multimedia Architecture for Video,Image and Sound),which is

an on-going research project to provide an integrated and networked approach to multimedia content based retrieval

and navigation.Demonstrations are provided of the two presented retrieval methods in action.

1Introduction

With a trend towards digital archiving of textual and visual documents,the management of large databases can be very dif?cult without an acceptable way of indexing these,in particular to simplify retrieval of relevent information.For many years our group have been working on the development of an innovative multimedia authoring tool and open-hypermedia system,Microcosm[2],which has now been commercialised.The MA VIS(Multimedia Architecture for Video,Image and Sound)[10,9]project is a programme of research to extend the open hypermedia idea to non-text media.Moving to a portable Java based implementation,MA VIS-2introduces the use of a multimedia thesaurus [11],intelligent agents and the ability to distribute processes,and perform matches in parallel,by utilising HTTP messaging.The Multimedia Thesaurus(MMT)[11]consists of a network of traditional thesaurus relations between text representations of concepts,augmented by representations of concepts from non-text media.This can extend the range of a query using synonym substitution and enhance cross-media navigation through the concept network.

Each media type supported by MA VIS(images,text,video,etc.)may have multiple feature extraction methods,for different features of the media,and each feature extraction technique is contained in a module,which is responsible for creation and matching of signatures.By preprocessing selections into signatures,and storing and indexing the signatures in a database,time can be saved during the matching process.

This paper describes how the spatial colour distribution module uses a variety of cues in the image to create and match signatures facilitating retrieval of images based on their spatial colour distribution.

2Related Work

To humans,the colour of an object is the most easily identi?able property-in comparison to texture and shape.In the computer representation is is also easily accessible.It is therefore not suprising that there are many colour-based image retrieval systems in development.

IBM’s QBIC System[3,12,13]is probably the most well known content-based retrieval engine,and uses colour, shape,and texture.The use of large areas of solid colour,drawn as rectangles in a paint-package,are transposed to a grid and used as a template for matching against images in a database.Queries based on histograms from other images is not supported.

Microcosm is a commercial product of Multicosm,Ltd.See https://www.doczj.com/doc/822565885.html,

Challenge of Image Retrieval,Newcastle,19991

PicToSeek[6]is a content-based image search system,designed for use on The Web by the Intelligent Sensory Information Systems research group,at the University of Amsterdam.The system uses a colour model that is colour constant-that is,it is independent of the illumination colour,shadows,and shading cues.PicToSeek is,however,only concerned with the whole image histograms,and does not allow spatially oriented queries.

Virage[7]is a system produced by Virage Inc.that performs content-based navigation on video and images,using colour,texture,composition(colour intensity distribution),and structure(shape layout).

VisualSEEk[18]is a content based search engine designed at the Center for Image Technology for New Media,at Columbia University,New York.The system uses colour set back-projection to extract regions of colour from images. Colour back-projection is a way of automatically extracting salient regions,by quantizing the image based on the ‘colour sets’-which are thresholded histograms.Because colour sets are binary,the histogram matching functions can be reduced which allows ef?cient indexing.VisualSEEK allows spatial-colour retrieval based on a query built from areas of solid colour,and semantic relations between those areas.

Color-WISE[14]is an image similarity retrieval system which allows users to search for stored images using matching based on the localized dominant hue and saturation values.It uses a cunning?xed segmentation of over-lapping elements to ensure that the matching is slightly fuzzy.The system computes seperate histograms for hue, saturation,and intensity,and reduces their size by?nding their area-peak-basically removing noise that is small amounts of isolated colours.Color-WISE uses Microsoft Access to perform the database functions,and uses a simi-larity metric based on IBM’s QBIC system.Querying in Color-WISE is achieved with query-by-image.

The Digital Library Project[5]taking place at the University of Berkeley,California,uses low-level grouping techniques to create“blobs of stuff”,which can be texture,colour,or symmetry.The blobs can be matched against their content,and their position,and it is possible to use high-level techniques to analyse the semantics of the blobs (such as where they are in relation to other blobs),and conclude what they might represent.

Image-MINER[1]is an image and video retrieval system developed by the AI group at the University of Bremen. Their colour indexing system for images uses local histograms in a?xed grid geometry.Further grouping of the?xed elements occurs to get‘color-rectangles’,which are signatures for their input images.The colour based segmentation module,is part of the larger Image-MINER system which includes video retrieval methods,including shot detection and subsequent‘mosaicing’.

Haung et al.at Cornell University,has developed the idea of a‘correlogram’[8]which is a single feature incor-porating both spatial-correlation and colour information.The spatial relationships index on the probability that a pixel will be of a particular colour,when a given pixel is of another colour,and a certain distance from that pixel.

3Spatial Colour Matching using a Grid

Using a simple histogram based on the whole image content is not suf?cient for accurate colour-based retrieval of images,because it has no spatial dimension in the match.This inability to distinguish where the colours are located has led to many variations which take into account the spatial distribution of colours in the image.Taking the location of colour into account,is more likely to retrieve similar images—in effect,adding the extra dimension of matching decreases the possible number of matches and increases the precision of retrieval.

For the spatial colour matching,the starting point was a grid mechanism similar in concept to the Image-MINER and,to some extent,the QBIC approach.

To create a signature,the image is divided into a grid,each element of which contains part of the query image.The grid is overlaid on an image,meaning that a grid element will be of a different size for different images of different sizes.Each grid element has a histogram calculated for it based on the currently selected colour space.The histogram is normalised to ensure that histograms from different sized images are comparable.By using a simple grid mechanism the user interface is kept simple to use and learn.

To save processing at match time,images are usually converted before hand and stored into a database.The module can then retrieve signatures of previous images and match them against the signature of the query image.This requires only the query image to be converted at match time.

Challenge of Image Retrieval,Newcastle,19992

4Colour Histogram Matching

Another desicion which is crucial when creating histograms,is which colour representation to use.The common colour models to use are RGB,HSV(and similar),YUV,CIE L*u*v*(and derivations,like CIE L*a*b*,CMC,BFD and M&S),and Munsell.As a compromise between computational effort,and relevance to the human visual system, the HSV colour model has been chosen for use in the work so far.

To convert RGB pixels into the HSV colour space we use the fast implementation as described in[4]which approximates the HSV cone to a hex-cone,with the corners on the colours red,yellow,green,cyan,blue,and magenta.

Although we have chosen to use the HSV colour model,we have ensured that this is not a?nal design decision. To this end,the colour model is selectable,but the colour space in to which the query is quantised,must be the same as the space which was used to create the database,so that the histogram match has some meaning.

Matching of histograms uses the histogram quadratic distance technique(weighted Euclidean distance),developed by IBM for their QBIC system[12]for histogram matching on full images,and objects in images.It takes into account the similarity of colours in different bins of the histogram,i.e.how similar the red bin is to the orange bin,which simple histogram distance measures based on the Minkowski metric do not.Each n-dimensional histogram becomes a one-dimensional row vector,the query histogram,,and the histogram to match against,.The histograms are normalised so that the area beneath them is1.The differences between the frequency stored in each bin,,is taken, and becomes another one-dimensional vector,,as shown in equation1.

(1) The match between a bin in histogram and histogram is weighted by the distance between the bins in the colour space.If the distance between the furthest two bins in the colour space is,and the distance between bin and bin(which is dependent on the colour space)is given by,then the weighting matrix,,is given by

equation2.

(2) Matching between the two histograms is then a matter of matrix algebra,given by equation3.

(3) The single matrix cell results in a similarity measure,,where,and where is an identical match. Conversion to a percentage message of similarity,,is given by equation4.

(4) 5Matching using a Grid

For any single query,a particular match can be made upon a multiple or single grid element of the query image.Also, a match can take place with or without regard to the location of the grid element.Performing a spatial match,a single grid element in the query image,,will be matched against the grid elements in the database images at that particular position,.If multiple elements are selected,each is compared to its equivalent in the database image and an average taken across the matches.This is expressed in equation5.

(5)

A non-spatial match with only one selected grid element,will match all the grid elements in the database images against that single element in the query image.This returns multiple matches which will give rise to the problem of how to aggregate the matches.The most sensible way,in this case,would be to choose the highest match and give it as the?nal measure of similarity-so,if we are looking for a very red square,if there is a red square in the database

Challenge of Image Retrieval,Newcastle,19993

image somewhere,it will be a good match.In a non-spatial match with multiple grid elements selected,the selected elements are each matched against all elements in the database image.For any particular selected query element,the maximum from all the similarities for that element is chosen as the best match.The average is taken over them all to be the match for the whole query.

(6)

6Spatial Colour Matching using Quad-Trees

A problem that was brought to light in testing was that sometimes deciding whether a particular grid element should be selected for a match was dif?cult,particularly if it contained a strong colour gradient(for example,it had an edge running through it).

To attempt to overcome this problem the use of quad-trees was introduced to localise changes in the features in an image during segmentation.

A quad-tree is a simpli?cation of the idea of the split and merge algorithm and the T-pyramid[19].Quad-trees involve recursively dividing the image into quadrants until all elements are homogenous,or until a prede?ned,“grain”, size is reached.Again,a histogram is built for each element in the quad-tree.This means that,in general,the quad-tree method requires more storage than the grid method(and hence longer match times).Unlike the split and merge algorithm which is used for object delineation,the divided areas are not merged again,even if they are adjacent and their total area would?t the homogeneity criteron.This ensures no loss of spatial data.

To match quad-trees with regard to spatial location of elements,requires some cases which did not need to be considered with the grid method.In particular,it is possible that the query selection is smaller than the nearest available element in the quad-tree retrieved from the database.It is also quite possible,that the opposite occurs,and the user’s selection in the query is larger than that in the retrieved quad-tree.To recognise these cases,we use the leaf-code,as proposed by Sonka et al.in[19].Each quadrant is numbered from one to four,left-to-right,top-to-bottom—that is,the top-left quadrant is quadrant-one,and the bottom-right quadrant is quadrant-four.This allows every node in the quad-tree to be represented by a unique code,its length representing the depth at which the node resides.It is then possible to?nd the nearest node in the quad-tree in the database to the node which is currently being matched from the query image’s quad-tree representation.This method also increases the speed of spatial-matching.

If the nearest node in the database quad-tree is the same depth as that in the query node,then they can be directly matched.If the nearest node in the database quad-tree is not a leaf node,then the histograms below that node are summed,so that,effecively,the area in the query image and the area from the retrieved database image are equivalent and can be directly matched.The most awkward case is that when the query quad-tree node is at a greater depth than the nearest match from the database image.Rather than trying to reduce the size of the retrieved selection, the pragmatic approach,suggests that the smaller query area can be directly matched with the larger area from the database.This would seem resaonable because for the images to be segmented in this way,the features must be close to homogenous,and further division would give no better results.

The matching of quad-trees without regard to the spatial location of the elements being matched,is as simple as doing it with grid elements,however it is likely there will be more elements to match,meaning the time taken to match the same database could be longer.

7User Interface

To ensure that this functionality is all readily available and able to be used,the user interface has been designed in such a way to make selection and querying of images as simple as possible.We have made sure that all the functions that will be required in constructing a query have all been placed in view,to avoid use of menus.The main query viewer for the grid method can be seen in Figure1,and for the quad-tree method in Figure3.

Helper tools have been built around the main interface which facilitate creation of a colour,and also the copying of a selection in another image,into an element in the query image.This allows the user to build a completely new Challenge of Image Retrieval,Newcastle,19994

Figure1:The main viewer for the grid method,with an area of the car selected for matching upon,in a non-spatial match.With the grid lines on,it is possible to see how the images are segmented with this method.

query,not based on a single example image.A simple results viewer was built so that the matches could be viewed in similarity order,and that the best matching grid element be visible if appropriate.

Copying data from colour selectors,or from other areas of images has been achieved using a method which we have called the“paint bucket”.This allows users to place into it anything from a helper tool(the colour selector,or image viewer).The user can then change to“paint”mode on the interface which will allow them to copy the contents of paint bucket into elements in the query image.Changing back to“select/deselect”mode lets them change which grid elements are selected for querying.All the windows in the system have been made non-modal so that at any time the user can perform any task,which includes multiple results windows,and image viewers.

8Experimental Results

As explained in[6],the hue component of a colour is invarient to highlights and shadows,and it is the colour which matters mostly in a search of this kind.After testing various quantisations of the histogram,it was decided to use a segmentation of,with12hue bins,giving108bins in a histogram.This division provided enough accuracy to be able to distinguish well between objects,as well as keeping the size of a histogram to a minimum.

Figure1shows the main viewer for the grid method.Figure3shows the main viewer for the quad-tree method. Both have equivalent areas of the image selected.We can perform a non-spatial match on these areas using both methods.This will look for the small selection in all of the other images,and in any position within these images. Figure2shows the top three images of the grid-method.Figure4shows the equivalent results for the quad-tree method.It can be seen that the quad-tree method has found the correct images more accurately,due to the ability to have small elements,which are speci?c to a feature in the image.

We can perform a spatial match using the same tools.In Figure5we have selected part of the sky using the grid method.In Figure7we have selected the equivalent part of the sky using the quad-tree method.The results are shown in Figure6and Figure8for the grid method and the quad-tree method,respectively.It can be seen,both methods have Challenge of Image Retrieval,Newcastle,19995

Figure2:The results from the non-spatial match of the area in Figure1,using the grid-method. retrieved relevant images.This suggests that for spatial matching,the grid method is as good as the quad-tree method, and,in practice,the match times were almost identical.The quad-tree method has advantages when used to perform non-spatial matching and also to make the user-interface friendlier,but the grid method is much faster at non-spatial matching due to the smaller amount of data to process.

Further functionality can be achieved by using combinations of the functionality in these spatial-colour matchers. For example,by selecting all the grid elements in a query image,and performing a non-spatial match,is the equivalent of using the global histogram technique for image retrieval.

9Conclusion and Future Work

A novel approach to image matching and retrieval based on spatially related colour cues using a grid,and quad-trees has been presented.Initial experiments with the approach suggest that it will enhance the precision of image retrieval over other colour retrieval methods,and improve the reliability of content based navigation.

Further work needs to be directed towards a faster method of database retrieval and indexing.A way to achieve this may be to use a pyramid of histograms by using the quad-tree idea,and performing initial esimates of colour matches between images,to remove those that are sure not to yield good results.Rather than using colour,a generalised spatial-feature matcher could be designed using the ideas shown here;rather than extracting colour from a query selection, extracting texture would allow for a spatial-texture matcher.An extension to this system might be to use fast object delineation techniques to select general shapes for matching upon which would require a more complex algorithm for object location matching.

However,it can be seen that there is a limit to the information that can be presented by roughly segmenting an image based on generalised feature methods.Work is taking place on using scale-space methods to produce an object tree based on the features in the image.A matching algorithm for these object-trees could provide the basis for a more generalised feature matcher.

Extensions to MA VIS are also planned that will increase the ef?ciency of the database retrieval,by using a com-mercial database to store all the data.

Challenge of Image Retrieval,Newcastle,19996

Figure3:The main viewer for the quad-tree method,with an equivalent area of the car selected for matching upon in a non-spatial match.With the grid lines on,it is possible to see how the images are segmented with this method.

10Acknowledgements

The?rst author is grateful to the Engineering and Physical Sciences Research Council(EPSRC)for the support of a research studentship.

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Figure4:The results from the non-spatial match of the area in the Figure3,using the quad-tree method.The retrieved results are more precise than those using the grid method.

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Figure5:The main viewer for the grid method,with an area of the sky selected for performing a spatial match upon.

Figure6:The results of a spatial match of the query presented in Figure5using the grid method.The results are very good for the simpler method.

Challenge of Image Retrieval,Newcastle,19999

Figure7:The main viewer for the quad-tree method,with an area of the sky selected for performing a spatial match upon.

Figure8:The results of the spatial match of the query presented in Figure7using the quad-tree method.The results are good,with as good recall and similar precision to those using the grid method.

Challenge of Image Retrieval,Newcastle,199910

小学英语“What colour is it”教学设计

一、教学设计说明: 本节课选自小学英语三年级上学期Lesson 9“What colour is it? ”.本课时主要学习用英语表达事物的颜色,其内容贴近小学生的生活和学习实际,有利于他们在真实的语言情境中培养运用英语进行交际的能力。教材强调学生在学习过程中的感悟、体验、实践、参与以及思维能力的发展,突出兴趣培养,重视学科内容的有效融合,注重培养学生自主学习的意识和能力。 二、学情分析: 本课的教学对象是刚刚涉足英语的三年级小学生,学生的好奇心强,大部分学生对于英语有着强烈的兴趣,敢于大胆开口说英语,愿意同他人合作,在交际中学习英语,感悟英语,在游戏中体验英语,认识英语。但三年级的新生刚刚入学,自制力较差,注意力不易集中。 三、设计理念: 《英语课程标准》中明确指出,基础教育阶段学习英语的目的旨在激发学生学习英语的兴趣,培养他们学习英语的积极态度,使他们建立初步的学习英语的自信心;培养学生一定的语感和良好的语音、语调基础,使他们形成初步用英语进行简单交流的能力,为进一步学习打下良好的基础。 要激发学生学习英语的兴趣,我们就要让学生变被动为主动地跟着教师参与教学活动,运用一些学生喜闻乐见的教学方法及措施。因此,教师在教学中发挥主导作用,以学生为主体,采用直观情境教学法、TPR(全身反应法),活动式教学法。充分采用了任务型教学途径,为学生提供了较大的实践空间,能较好地发挥学生的主动性和创造性。任务型教学途径适合多种活动类型和组织形式(如,游戏、比赛、竞猜等),它不但可以独立操作,也可以利于合作学习,使学生在乐中学、学中乐。 四、教法学法设计: 在本课中,我采用任务教学法,直观演示法、情境教学法、游戏教学法相结合的方法。在教学设计中一开始就把任务交给学生,让学生在任务的驱动下学习语言知识,利用多媒体课件,实物等创设情境,采用学生易于接受的游戏等方式组织课堂教学,提高了课堂效率。 生活化的情景有利于培养学生的语言运用能力,“情景交际法”是本课主要的教学方法。通过颜色学习,由green(绿色)red(红色)yellow(黄色)引向对学生的交通意识教育,打破学科本位的传统模式,将实际生活渗透于英语教学之中。 TPR教学法主要针对儿童的身心特点设计的,运用该法设计红绿灯绕口令,能充分调动学生的各种感官参与学习,有助于对知识的理解和内化。在学习方法上,主要运用全程式自主学习方式,从学生的需求入手,将学习内容与学生的日常生活经验相结合,在协同学习的环境中体验、参与,从而建构个人的经验,创造个人的知识。 五、教学内容: 1、五个新单词: blue, green, yellow,orange, red. 2、句型: What colour is it? It’s… 六、教学目标: (一) 知识目标:1、能听、说、认读五个颜色单词blue, green, yellow,orange, red,并能在实际情景中进行运用。 2、能掌握句型:What colour is it? It’s…,并且能灵活运用。 (二)技能目标:让学生在游戏活动中熟悉运用所学颜色单词,并能对身边的事物进行简单的描述。让学生学会用What colour is it ?和It’s…来提问和回答周围的事物的颜色。培养学生在课堂上进行自主评价的能力。 (三)情感目标:关注学生的内在需要,在活动中培养学生的协作精神和竞争意识,培养学生热爱生活的积极情感态度。

解析雅思阅读信息配对题

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colours微评课 一、新旧结合,复习导入。 每节课的教与学都不是孤立的,它有承上启下的作用,是旧知识的拓展与深化。因此,在新课开始之前,对与本节课内容有关的知识与技能进行复习与巩固,既能弥补学生旧知识掌握中的缺漏,又能使学生很快地投入新知识的最佳学习状态。微课中教师主要教授的句型是What’s your favourite colo ur?及其回答My favourite colour is_______.教师在引出句型之前运用卡片复习了颜色单词,唤醒了学生的旧知,同时为本节课句型学习做好知识准备。 二、游戏操练,突破难点。 在英语单词教学中,如果总是采用教师领读、学生跟读的方法,学生会感到枯燥,所以不太愿意跟读。低年级学生活泼好动,乐于接受新奇、趣味性强的事物,在教学单词时融入游戏对帮助他们理解与母语迥异的另一种语言是必不可少的,是英语教学的重要手段。微课中favourite这个单词是今天所学的句型中的重点和难点,学生对其意思的理解和单词的发音掌握起来都具有一定的困难。教师在操练单词时设计了一个小游戏,出示笑脸学生就说favourite,不是笑脸就不能说,这个游戏可以防止学生注意力不集中,跟

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基本配色——平静 -----------------------------------------------------------------------------------

在任何充满压力的环境里,只要搭配出一些灰蓝或淡蓝的明色色彩组合,就会制造出令人平和、恬静的效果。 中间是淡蓝的配色设计,会给人安心的感觉,因为它看起来诚实、直接。 带着明色的寒色可保持安宁、平和的感觉。补色和这些强调平静的色彩在明暗度方面一定要类似,这点很重要,因为要是色彩太鲜明,会制造出不必要的紧张。

基本配色——强烈 -----------------------------------------------------------------------------------------

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