Shape-Based Image Retrieval in Logo Databases (Final Report) Jusub Kim December/27/2002 CMSC725 Geographical Information Systems and Spatial Databases Instructor: Dr. Hanan Samet

gorithms to use shape similarity in retrieving images and the state of the algorithms are still primitive. In this paper, shape-based image retrieval problems are handled especially in black-white image databases. The interior model for connected black-white images is presented and two approaches to capture the shape of images are introduced based on the model.

1 Introduction In recent several years, contents-based image retrieval has been studied with more attention as huge amounts of image data accumulate in various fields, e.g.,medical images, satellite images, art collections, and general photographs. Image databases are usually very big, and in most cases, the images are indexed only by keywords given by a human. Although keywords are the most useful in retrieving images that a user wants, sometimes the keyword approach is not sufficient. Instead, Query-by-example or pictorial-query approach make the system return similar images to the example image given by a user. The example images can be a photograph, user-painted example, or line-drawing sketch. In this method, images are retrieved by their contents: color, texture, shape, or objects. Thus, the degree of similarity between query images and images in databases can be measured by color distribution, texture distribution, shape similarity, or object presence between the two images. There have been many works done with color and texture property. However, seldom works have been done with shape similarity since we need to have good segmentation and recognition al-


Related Works

There have been some efforts in a structural way in retrieving images using shape property. In [1], A. Soffer proposed a pictorial query specification technique that enables the formulation of complex pictorial queries including spatial constraints between query- image objects which are predefined symbolic images and contextual constraints which specify how many objects should be in the target image. The predefined symbolic query-images are represented by shape feature, e.g., moment, circularity, eccentricity, rectangularity, etc. In [2], A. Folkers extended this tool to permit query-images that have spatial extent such as ellipses, rectangles, polygons, and B-splines. The query-images are represented by fourier descriptors which serve powerful boundary-shape representation tools because of invariance property in affine transformation. However, there is a limit to expressing an object by its boundary because the boundary itself does not represent inside shape feature of 1





Figure 1: Outer and inner boundaries: a) an original image, b) an outer boundary and c) explicit inner boundaries d) an implicit inner boundary nected component. b) shows its outer boundary, c) explicit inner boundaries and d) an implicit inner boundary. In c), the white circle and two trapezoids correspond to the white holes and the black trapezoid corresponds to the black hole. Implicit inner boundaries include any possible closed curve except for explicit inner boundaries and the outer boundary. So, there are more than one implicit inner boundaries in a) including d). Note that the black trapezoid(a black hole) is considered as giving an inner boundary since we clearly recognize the boundary. In other words, explicit inner boundaries mean that we can explicitly recognize the boundaries while not in implicit inner boundaries. With above assumption, an interior model for connected black-white images is described as follows. The interior model applies to each connected component in a black-white image. We can build a model based on a designer’s point of view. We have two primitives entities,

the object. In [3][4], A. Soffer proposed a method using negative feature to represent shape of blackwhite logo images. In the papers, a bounding border is added around a logo and the logo features included the negative feature between the bounding border and the original logo. Local and global shape features are computed for each component using several shape descriptors except for the Fourier descriptor.

3 Interior Model for Connected Black-White Images A black-white image consists of one or more than one connected components. We can assume that one connected component’s shape is represented by its outer boundary and inner boundaries which are all the other boundaries except for the outer boundary. The inner boundaries can be classified into two: 1. Explicit inner boundaries: white hole’s boundaries and black hole’s boundaries

1. Transparent patch. 2. Opaque patch (Black/White).

2. Implicit inner boundaries: any other closed curve except for 1 and the outer boundary.

Just as the word itself means, a transparent patch is Figure 1 shows us an example. a) shows an orig- a closed curve by a black line and it’s part of border inal black-white image which consists of one con- is occluded when a opaque patch lies upon it’s some 2

Transparent and Transparent



Transparent and Opaque



Opaque and Opaque





Table 2: Examples of the relationship model in Table 1. Patch Type Transparent Transparent Transparent Transparent Opaque Opaque

part. Opaque patch has two kinds: black and white. Both of those are a closed area and none of it’s inner area and border is occluded by a transparent patch. But, one opaque patch can occlude the other opaque patch. With these primitive entities, we can establish the relationship model between the two primitives as shown in Table 1. In Table 2, two examples are shown corresponding to each relation. For example, in the contiguous transparent-transparent case (a), the first example shows the case that one transparent circle is contiguous with one mountain-shaped transparent patch on the bottom arc and the second example shows that one heart-shaped transparent patch is contiguous with one star-shaped transparent patch on three points. As another example, the first image in the overlapped opaque -opaque case (f) shows that one black opaque circle is overlapped with three white opaque triangles and the second image shows that one black opaque circle is overlapped with one slim black opaque ellipse.

Patch Type Transparent Transparent Opaque Opaque Opaque Opaque

Relation Contiguous Overlapped Contiguous Overlapped Contiguous Overlapped

Table 1: Relation model between two primitives in a connected black-white image.

lapped with a white opaque circle or being contiguous with a transparent circle. Although the model is based on the relationship between two entities: transparent and opaque patch, we can model every connected black-white image as a combination of this two relation model. For example, Figure 1 can be analyzed as one transparent triangle being overlapped with one white opaque circle, two white opaque trapezoid and one black opaque trapezoid. Let’s look at the image in Figure 2 consisting of more than one connected components. It can be analyzed as having two connected components: one is a big black opaque overlapped with a small star-shaped white opaque, and the other is two black opaque strips adjacent to one transparent strip.

However, it’s categories are not disjoint which means the same image can fall into several categories. For example, in a) of Table 2, a transparent mountain-shaped patch is contiguous with a transparent circle. But, it can be also considered as a white opaque mountain-shaped patch being over3


Negative shape extraction

We denote negative images by white holes in one connected component. Figure 3 shows a negative image example. What can we do with a negative image approach? As we see in Table 2 based on the proposed interior model, using negative image, we can find all white holes’ boundaries in all the cases Figure 2: Image consisting of more than one con- of Table 2. For example, in Figure 1, using neganected components tive image, we can find all white holes’ boundaries shown in (c) except for the black opaque trapezoid. To get negative components of a connected component in an image, for every connected component, we get a filled area image which is filled with black pixels enclosed by its outer boundary, and just do XOR operation between the filled area image and the original connected component image. Then we get one or more than one negative components in the original component corresponding to the original Figure 3: Original image and its two negative im- component’s shape composition. The boundaries of negative components are handled equally with other ages. boundaries: the outer boundary and black opaques’ boundaries which will be explained later.

4 Feature extraction with Fourier Descriptor 4.1.2

4.1 Image Preprocessing

Line deletion

With the negative approach, we can not find black holes’ boundaries such as the black opaque trapezoid in Figure 1. However it is easily detected by line deletion. We define the line as connected black pixels of which thickness is less than predefined width. We apply morphological closing operation to detect and delete the line. By setting appropriate kernel size which means defining the lineness and simply doing the closing operation, we can delete lines and get a black opaque’s(black hole’s) boundary. Figure 4 shows a line deletion example.

As mentioned in the previous section, if we assume that the shape of a connected black-white image is represented by its outer boundary and inner boundaries, it is important to find inner boundaries for the shape extraction of black-white images. Based on the model proposed in the previous section, two approaches to capture the explicit inner boundaries are introduced and their limitation is discussed based on the model. 4






Figure 4: An example of line deletion: (a) an original Figure 6: Meaningful implicit boundaries: (a) an image and (b) an image after line deletion. original image and (b) explicit boundaries and (c) implicit boundaries



can be redundant. However, in the overlapped twotransparent patch case, we may not catch meaningful shape information with those approaches for explicit boundaries. In that case, meaningful shape can exist on implicit boundaries. Let’s look at Figure 6. We analyze the image as one transparent circle being overlapped with one transparent triangle based on the proposed model from a designer’s point of view. That means only the two shapes(circle and triangle shape) can be meaningful. However, as seen in (b) of Figure 6, we cannot find them on explicit boundaries. The meaningful shape information hides on implicit boundaries. Furthermore, more meaningful shape information can lie on implicit inner boundaries if the model is extended to allow more than two entities related in one connected component. For example, in two contiguous transparent patch case, if another transparent patch is laid adjacent to the inside patch as seen in Figure 7, then we can just find partial set of meaningful inner boundaries using the proposed approaches for explicit boundaries. As seen in (b) of Figure 7, we cannot find the implicit star-shaped boundary. while possible in (a). Although it has a limited power, the two approaches based on the interior model give us enough interior shape information of a image since in most cases, meaningful shape has explicit boundaries and that is also what a designer


Figure 5: Redundant implicit boundaries: (a) an original image and (b) explicit boundaries and (c) an implicit boundary 4.1.3


With above two approaches, it is obvious that we can find explicit inner boundaries, i.e. white hole’s and black holes’ boundaries. However, it is impossible to catch implicit inner boundaries in all the cases of the Table 2. Let’s look at Figure 5. We can find explicit boundaries in (b) ,but not implicit boundary in (c). However, we can think of this problem from the proposed model based on a designer’s point of view. Figure 5 can correspond to two contiguous transparent-transparent case, where one star-shaped transparent patch is contiguous on three points with one heart-shaped transparent patch. It gives us a hint about the shape information, i.e., only heart and star shape can be meaningful while all implicit boundaries in (c) of Figure 5 can be meaningless, which means that if we find implicit boundaries, then they 5





Figure 7: (a) Two contiguous transparent patches and Figure 8: A case in which a negative feature is redun(b) three contiguous transparent patches are related dant: (a) an original image and (b) a negative image. in one connected component descriptor was used to represent shape of logo images. In this paper, the same methods as in [2] are wanted. applied for describing each boundary shape. The Fourier descriptors have a powerful property which 4.1.4 Other issues is invariant against translation, scaling, rotation, and Applying the introduced methods, we can get many starting point. boundary features. But, it is very hard to determine which component’s shape is important or the one that 5 Sample queries and results a designer intended when making the image. So, we can apply some heuristic methods to reduce the size 5.1 Environment of boundary feature set. We can assume that a feature is an important one when it’s size is at least some The components of the logo images are stored in value and by doing the size filtering, we can reduce SAND(spatial and non-spatial data) which is a protothe number of features. On the other hand, as seen type spatial database system developed at University in Figure 8, when a connected component consists of of Maryland[6]. In SAND, data is stored in tuples one transparent patch, it has one negative component consisting of attributes for geometric entities such as also which is redundant if added to a feature list. So, points, lines, polygons, etc. in addition to traditional by eliminating duplicate features based on a shape ones like integer, floating point number, and characdescriptor such as fourier descriptor, we can prevent ter strings. SAND can index both spatial and nonspatial data using different methods such as PMR the case. quadtree, R-trees, and K-d trees. For the experiment, the same method is used as in [2], where each com4.2 Fourier descriptor ponent’s shape is stored in SAND in two ways: clasOne of the well established method for describ- sification and abstraction. While in the classification ing shape of closed curve is the Fourier descrip- mode the shape is classified into one of the four basic tors. In [5], Daniel and et al. compared several type shapes: polygon,ellipse, rectangle, and B-spline transformation-invariant shape descriptors and eval- and the result is stored, the normalized Fourier deuated them reporting superior ability of Fourier de- scriptors of the shapes are stored in SAND in the abscriptors. Based on the evaluation, in [2], the Fourier straction mode. The pictorial queries specify which 6

shapes should appear in the target image as well as spatial constraints on the distance between them and their relative position. The retrieval uses the classification or abstraction mode which is selected by users. The logo set used for sample queries consists of hundreds of logos that are registered as trademarks at the US Patent and Trademark Office. (a)

(c) 0.091

5.2 Sample queries and results It is not easy to evaluate the proposed model quantitatively. So, in this section some sample queries and the retrieval results are provided to show that the algorithms based on the model are working in real databases. For the following 4 queries, spatial constraints are set to any relation and any distance between components and contextual constraints are set to all symbols and maybe others, refer to [2]. In each figure, (a) shows a query image and each value below the retrieval image indicates how much different two images are. In Figure 9, the images were retrieved only by their outer boundaries. (c) was retrieved by considering a series of circles as connected due to some image processing misleading. In Figure 10, (b) can be analyzed as a contiguous transparent-opaque case(or opaque-opaque case also), and the image was retrieved by the negative cross-shaped feature. On the other hand, (c) can be viewed as an overlapped transparent- transparent case and the image was retrieved by the inside cross-shaped negative feature. In Figure 11, (b) and (d) were retrieved by black opaques’ outer boundaries while (c) was retrieved by negative circles. (c) can be analyzed as a contiguous transparent-opaque case (or white opaque-black opaque also). In Figure 12, (b) can be viewed as an overlapped white opaque and black opaque case. It was retrieved by three negative rectangles.

(b) 0.042

Figure 9: Sample Querie 1: Images are retrieved only by outer boundaries.


(b) 0.082

(c) 0.121

(d) 0.132

Figure 10: Sample Querie 2: (b) and (c) are retrieved by negative features




(b) 0.072

Concluding Remarks

For a shape-based image retrieval in black-white image databases, it is demanded to extract inner shape information in each connected component of the image. In this paper, the interior model for the connected black-white image was proposed, and based on this model, two approaches were introduced to extract interior shape information. The proposed model shows us the extent to which the approaches can find interior shape. The sample querie test shows us the reasonable results. In future, spatial constraints are required to be extended to handle inclusion relationship between two objects since extracted negative components make new inclusion relationship between the original components and the negative components.

(c) 0.094

(d) 0.116

Figure 11: Sample Querie 3: (c) is retrieved by negative feature

References [1] A. Soffer and H. Samet ”Pictorial query specification for browsing through spatially-referenced image databases” Journal of visual languages and computing, 9(6), pp.567-596, Dec. 1998 [2] A. Folkers and H. Samet ”Content-based image retrieval using fourier descriptors on a logo database” Proceedings of the 16th International conference on Pattern Recognition, pp. 521-524, 2002


[3] A. Soffer and H. Samet ”Negative shape features for image databases consisting of geographic symbols” 3rd International Workshop on Visual Form Processing, pp 569-581, 1997.

(b) 0.118

Figure 12: Sample Querie 4: (b) is retrieved by negative feature [4] A. Soffer and H. Samet ”Using negative shape features for logo similarity matching” Proceeding of the 14th International Conference on Pattern Recognition, pp 571-573, 1998. 8

[5] D. Heesch and S. Ruger ”Combining features for content-based sketch retrieval - a comprehensive evaluation of retrieval performance” Proceeding of the 24th BCS-IRSG European Colloquium on IR research,LNCS 2291, pp 41-52, 2002. [6] C. esperanca and H. Samet ”An overview of the SAND spatial database system”, submitted for publication.


Shape-Based Image Retrieval in Logo Databases

In recent several years, contents-based image re- trieval has been studied with more attention as huge amounts of image data accumulate in various fields,.

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