Applying Color Names to Image Description

International audiencePhotometric invariance is a desired property for color image descriptors. It ensures that the description has a certain robustness with respect to scene incidental variations such as changes in viewpoint, object orientation, and illuminant color. A drawback of photometric invariance is that the discriminative power of the description reduces while increasing the photometric invariance. In this paper, we look into the use of color names for the purpose of image description. Color names are linguistic labels that humans attach to colors. They display a certain amount of photometric invariance, and as an additional advantage allow the description of the achromatic colors, which are undistinguishable in a photometric invariant representation. Experiments on an image classification task show that color description based on color names outperforms description based on photometric invariants

Character Sequence Models for ColorfulWords

We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us

Weakly Supervised Domain-Specific Color Naming Based on Attention

The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.Comment: Accepted at ICPR201

MIRACLE-FI at ImageCLEFphoto 2008: Experiences in merging text-based and content-based retrievals

This paper describes the participation of the MIRACLE consortium at the ImageCLEF Photographic Retrieval task of ImageCLEF 2008. In this is new participation of the group, our first purpose is to evaluate our own tools for text-based retrieval and for content-based retrieval using different similarity metrics and the aggregation OWA operator to fuse the three topic images. From the MIRACLE last year experience, we implemented a new merging module combining the text-based and the content-based information in three different ways: FILTER-N, ENRICH and TEXT-FILTER. The former approaches try to improve the text-based baseline results using the content-based results lists. The last one was used to select the relevant images to the content-based module. No clustering strategies were analyzed. Finally, 41 runs were submitted: 1 for the text-based baseline, 10 content-based runs, and 30 mixed experiments merging text and content-based results. Results in general can be considered nearly acceptable comparing with the best results of other groups. Obtained results from textbased retrieval are better than content-based. Merging both textual and visual retrieval we improve the text-based baseline when applying the ENRICH merging algorithm although visual results are lower than textual ones. From these results we were going to try to improve merged results by clustering methods applied to this image collection

Looking at the Lanham Act: Images in Trademark and Advertising Law

Words are the prototypical regulatory subjects for trademark and advertising law, despite our increasingly audiovisual economy. This word-focused baseline means that the Lanham Act often misconceives its object, resulting in confusion and incoherence. This Article explores some of the ways courts have attempted to fit images into a word-centric model, while not fully recognizing the particular ways in which images make meaning in trademark and other forms of advertising. While problems interpreting images are likely to persist, this Article suggests some ways in which courts could pay closer attention to the special features of images as compared to words