Natural scenes classification for color enhancement

Naccari, Filippo; Battiato, Sebastiano; Bruna, Arcangelo; Capra, Alessandro; Castorina, A.

doi:10.1109/tce.2005.1405725

Cited by 21 publications

(15 citation statements)

References 5 publications

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“…textons [12]) which jointly consider shape and texture and geometric context cues of the thumbnails ( [6]). Also the possibility to use some ad-hoc classification system for natural scene will be considered ( [2], [7], [18], [19], [20]). • To improve the overall system accuracy by properly choosing a finite set of level for each involved visual feature (e.g., discretization).…”

Section: Discussionmentioning

confidence: 99%

Data mining learning bootstrap through semantic thumbnail analysis

et al. 2007

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The rapid increase of technological innovations in the mobile phone industry induces the research community to develop new and advanced systems to optimize services offered by mobile phones operators (telcos) to maximize their effectiveness and improve their business. Data mining algorithms can run over data produced by mobile phones usage (e.g. image, video, text and logs files) to discover user's preferences and predict the most likely (to be purchased) offer for each individual customer. One of the main challenges is the reduction of the learning time and cost of these automatic tasks. In this paper we discuss an experiment where a commercial offer is composed by a small picture augmented with a short text describing the offer itself. Each customer's purchase is properly logged with all relevant information. Upon arrival of new items we need to learn who the best customers (prospects) for each item are, that is, the ones most likely to be interested in purchasing that specific item. Such learning activity is time consuming and, in our specific case, is not applicable given the large number of new items arriving every day. Basically, given the current customer base we are not able to learn on all new items. Thus, we need somehow to select among those new items to identify the best candidates. We do so by using a joint analysis between visual features and text to estimate how good each new item could be, that is, whether or not is worth to learn on it. Preliminary results show the effectiveness of the proposed approach to improve classical data mining techniques.

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Section: Discussionmentioning

confidence: 99%

Data mining learning bootstrap through semantic thumbnail analysis

et al. 2007

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“…For instance, the presence of faces in an image can be inferred by the "skin appearance" in the pixel domain; likewise, an outdoor context can be inferred by "sky" and/or "vegetation" appearance [14]. Specifically, the following three types of visual information have been suggested to capture the appearance (see [8] for more details):…”

Section: Holistic Encoding Of Visual Contentmentioning

confidence: 99%

Exploiting visual and text features for direct marketing learning in time and space constrained domains

Battiato

Farinella

Giuffrida

et al. 2009

Pattern Anal Applic

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Abstract. Traditionally, direct marketing companies have relied on pretesting to select the best offers to send to their audiences. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the direct marketing industry has been under increased pressure to further optimize learning, in particular when facing severe time and space constraints. Taking into account the multimedia nature of offers, which typically comprise both a visual and text component, we propose a two-phase learning strategy based on a cascade of regression methods. This proposed approach takes advantage of visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS) show the effectiveness of the proposed methods that improve on classical learning techniques. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. The proposed methods can be used in any multimedia direct marketing domains in which offers are composed by image and text.

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“…Extensive researches were carried out on color image enhancement in the past with skin color enhancement using face detection [1,4] and color segmentation [2,3,6,9,10,11] making a significant contribution. In order to apply any processing at skin level, it is necessary to isolate the skin from the rest of the image details.…”

Section: Introductionmentioning

confidence: 99%

“…This is the similar approach to color region segmentation methods [10,11]. However, since our image enhancement technique controls the strength of sharpening and noise reduction especially around edge contour regions, we focus on how to make a clear-cut segmentation with a simple algorithm contrary to making the smoothing mask around the contour boundaries [10,11]. The edge information along with the color coded pixels is used for effective NR and sharpening of images.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement technique using color and edge features for mobile systems

Cho

Kim

2011

Digital Photography VII

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The paper provides a method of selectively controlling the strength of image noise reduction (NR) and sharpening in regions associated with specific colors that are visually impact on the human visual system. Since NR and sharpening operation can cause undesirable edge blurring or noise boosting in flat regions, the strength is adjusted according to the edge information. The proposed algorithm takes the following three stages: (i) color-based region classification, (ii) adaptive texture-based NR and sharpening, and (iii) H/W optimization for mobile image sensor compatibility. Experiments indicate that the proposed integrated algorithm platform provides significantly better results in terms of subjective visual quality.

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Natural scenes classification for color enhancement

Cited by 21 publications

References 5 publications

Data mining learning bootstrap through semantic thumbnail analysis

Data mining learning bootstrap through semantic thumbnail analysis

Exploiting visual and text features for direct marketing learning in time and space constrained domains

Image enhancement technique using color and edge features for mobile systems

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