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Table 3: Image Color Palette

in SHOW AND TELL: A Seamlessly Integrated Tool For Searching with Image Content And Text ABSTRACT
by Zhiyong Zhang, Carlos Rojas, Olfa Nasraoui, Hichem Frigui
"... In PAGE 4: ... For these reasons, we use an image color palette1. In our color palette, we use 12 colors as shown in Table3 . Notice that the color deflnitions are not categorical or clear-cut.... ..."

Table 1: Benchmarks on simple graph coloring problems.

in A Framework for Integrating Artificial Neural Networks and Logic Programming
by J. H. M. Lee, V. W. L. Tam 1995
"... In PAGE 36: ... The rst-fail principle [17] is also used for labeling in the CHIP program. Table1 gives the timing recorded for both CHIP and PROCLANN to solve the simple graph- coloring problems from 10- to 250-vertices. They both manage to solve the problems in a timely manner.... ..."
Cited by 13

Table 1: Benchmarks on simple graph coloring problems.

in A Framework for Integrating Artificial Neural Networks and Logic Programming
by J. H. M. Lee, V.W.L. Tam 1995
"... In PAGE 23: ... The rst-fail principle [17] is also used for labeling in the CHIP program. Table1 gives the timing recorded for both CHIP and PROCLANN to solve the simple graph- coloring problems from 10- to 250-vertices. They both manage to solve the problems in a timely manner.... ..."
Cited by 13

Table1, we note that about 43% of GIF images and 30% of the JPEG images are for categories other than TrueImages. We note that TrueImages that are larger than 6 KB (throughout the rest of this paper, we will refer to TrueImages that are larger than 6 KB as LargeTrueImages) make up 181.3 MB (45.1%) and 344.5 MB (78.3%) of GIF and JPEG data, respectively. Hence, throughout the rest of this paper, we place special emphasis on the characteristics of LargeTrueImages. To summarize, in this section, we analyzed our collection of images accessed in the Web for static characteristics such as their file size, geometry, number of unique colors and initial Quality Factor of JPEG images. We note that most of the GIF images (80%) are small (less than 6 KBytes). Simple transcoding filters such as bullets, icons, lines and banners remove about 45% of the GIF images from image transcoding consideration using simple HTML tags. We note that about 10% of the GIF images are LargeTrueImages. However, these images make up 45.1% of the data transferred for all GIF images. On the contrary, about 70% of the JPEG images are TrueImages. About 35% of the images are LargeTrueImages. These 35% of the images consume 78.3% of the JPEG data transferred. Hence, JPEG transcoding is promising for a larger set of images (35%) than for GIF images (10%) and also account for more of the consumed bandwidth.

in Transcoding Characteristics of Web Images
by Surendar Chandra, Carla Schlatter Ellis, Amin Vahdat, Surendar Ch, Ashish Gehani, Ashish Gehani, Carla Schlatter Ellis, Amin Vahdat 2001
"... In PAGE 13: ...73 69.82 Table1 : Image category distribution with the HTML ALT text. We advocate that advertisers should provide network friendly ALT texts to advertise their products.... ..."
Cited by 24

Table 2: Signatures to exemplify the derivation of the similarity metric

in Color-Based Image Retrieval Using Compact Binary Signatures
by Vishal Chitkara, Vishal Chitkara 2001
"... In PAGE 16: ...Table 2: Signatures to exemplify the derivation of the similarity metric Consider the signatures of images X, Y and Z as detailed in the Table2 . By simple inspection of the color densities (second column in the table) it is clear that images X and Z are more similar to each other than images X and Y.... ..."
Cited by 6

Table 3: Classi cation results obtained using combined shape and color features. 5. Conclusion Two approaches for wood defect recognition have been presented. We may conclude that although color information increases the ow of raw data and complicates the imaging system, it o ers some computational bene ts for lumber grading applications. The simple RGB centile features support straightforward and cheap elimination of most of the image data at an early stage of analysis. In practise, the necessary processing could be performed with the computational capacity of an intelligent camera. Shape related features require more computing, but provide a robust feature space, so that the illumination conditions need not be as strictly controled. Also, the presented features and classi er can tolerate errors in the training material without large deterioration in performance. With a signal processor the required lters can be implemented e ciently.

in Wood Defect Recognition: A Comparative Study
by Jouko Lampinen, Seppo Smolander, Olli Silven, Hannu Kauppinen
"... In PAGE 6: ... Normalization of the feature vector will eliminate any changes in the lighting level, and as a gray scale feature, changes in the color calibration has only marginal e ects. Table3 shows the confusion matrix, when the feature vectors were augmented by the color information as described in section 2.... ..."

TABLE 2. Color Region Table -- Butterfly color image

in unknown title
by unknown authors 1995
Cited by 37

Table 5. Simple Image AMS

in AMS – metadata for cultural exhibitions using virtual reality
by Nicholaos Mourkoussis, Martin White, Manjula Patel, Jacek Chmielewski, Krzysztof Walczak 2003
Cited by 1

TABLE 2. Color Region Table -- Butterfly color image

in unknown title
by unknown authors

Table 2 Lossless compression of color images

in Integer Reversible Transformation to Make JPEG Lossless
by Ying Chen, Pengwei Hao
"... In PAGE 4: ...Table2 gives the bit rates of color image experiments with different color transforms. ORCT and RGB only (without any color transform) are used in our iJPEG.... ..."
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