We begin by defining some terminologies. Throughout the report, we use the term photo interchangeably with image, all of which refers to an ordinary 2D image. We define collection as a set of photos and windows as cropped images. The representative set is loosely defined as a subset that captures representativeness, relevance and breadth in the original collection. Well we use two different notations for the random windows and sequential windows. For the random we use IN-C and for the sequence SN-C, where N is number of windows of each image and C is coverage need to be covered. For example with number of random or sequence windows that are 3 and with coverage 85%, we will represent as I3-85 if random images and S3-85 for sequence images respectively. A summary is a set of photos ordered by applying ranking mechanism and selecting any arbitrary number of images from the given representative set.

Given a set of photos P, Our goal is to compute a representative set RS □ P and then summary S □ RS such that S represents highly diverse representative images of set P.


In this section we introduce the selection criteria for the representative set and overview of the proposed solution.

Selection Criteria

As there is no accurate formal model which constitutes a “good” representative set and summary of a collection of images, we follow some simple heuristics that try to model and capture human attention.

These heuristics are as following:
• Images are taken at a location that provides views of some important objects or landmarks.
• Image is more relevant and should be included in representative set, if it matches with many other images of the collection.
• The representative set and summary should contain highly distinct or diverse images.

Overview of Proposed Solution

The proposed solution has three different phases. The first phase “Pre-cluster” is that we have three different techniques, followed by second phase “Clustering” phase which generates the representative set and the last phase is “Ranking mechanism” through which we generate the summary. The overview can be seen in the Figure 2.

In particular:
• We apply different pre-clustering techniques namely random and sequence in order to generate windows. The input will be an original large image set.
• We apply clustering to group appearance-wise similar images. The input will be windows generated from the pre-cluster phase. Then we fetch centroids of all clusters to generate a representative set.
• We apply ranking mechanism on the representative set and generate a summary by selecting any arbitrary number of top ranked images. So the input will be the representative set and the output will be a summary.

By applying these three phase, it will get the final outcome “summary”. One can see the graphical overview of the scenario in figure 3 for summary of 10 images.
Fig2Generating Representative_decrypted
Figure 2 : Overall Scenario of proposed solution.

As we described some heuristics in 4.1section, one may think that how can we obtain these heuristics by the proposed solution.

By clustering SIFT features; we can get groups of similar images. From each group (cluster) of images, we only fetch one image which is centroid of the cluster. So, now we have a set of images which are centroids of all clusters and should be visually distinct to each other. For more precise summary, we are applying ranking mechanism on top of the representative set in terms of highly dissimilarity.

Next we describe the algorithm in detail in the following sections. First, we discuss different preclustering techniques. Then we discuss the clustering algorithm and fetching centroids. Finally we describe how to rank each image of the representative set in order to generate summary.
Fig3Generating Representative_decrypted
Figure 3 : Overall Output Scenario

Representative APR 391%. Average APR for this type of loans is 391%. Let's say you want to borrow $100 for two week. Lender can charge you $15 for borrowing $100 for two weeks. You will need to return $115 to the lender at the end of 2 weeks. The cost of the $100 loan is a $15 finance charge and an annual percentage rate of 391 percent. If you decide to roll over the loan for another two weeks, lender can charge you another $15. If you roll-over the loan three times, the finance charge would climb to $60 to borrow the $100.

Implications of Non-payment: Some lenders in our network may automatically roll over your existing loan for another two weeks if you don't pay back the loan on time. Fees for renewing the loan range from lender to lender. Most of the time these fees equal the fees you paid to get the initial payday loan. We ask lenders in our network to follow legal and ethical collection practices set by industry associations and government agencies. Non-payment of a payday loan might negatively effect your credit history.

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