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Textual Based Image Search Computer Science Essay

In content-based image search, the goal is to retrieve the most similar images to a query image introduced to the system. Due to the complexity of multimedia contents, understanding of image is difficult. Extracting valuable knowledge from a large-scale multimedia database or web , so-called multimedia mining. Typically, in the development of an image requisition system, semantic image retrieval relies heavily on the related captions, e.g., file-names, categories, annotated keywords, and other manual descriptions. Unfortunately, this kind of textual-based image retrieval always suffers from two problems: high-priced manual annotation and inappropriate automated annotation. On one hand, high-priced manual annotation cost is prohibitive in coping with a large-scale dataset. On the other hand, inappropriate automated annotation yields the distorted results for semantic image retrieval. As a result, a number of powerful image retrieval algorithms have been proposed to deal with such problems over the past few years. CBIR is the newest technique for precise image retrieval. In general, the objective of CBIR is to represent an image conceptually, with like features such as texture, color and shape. These modern approaches for image retrieval are depend on the calculation of the similarity between the user’s image which give to the system and images on web or present on user’s system [1]. Despite the power of the search strategy, it is very conundrum to optimize the retrieval quality of CBIR within only single query process. To solve such problems, the users can select some very similar images to refine the image explorations one after another. The feedback procedure, relevance feedback repeats until the user is satisfied with the retrieval results. Although a number of Relevance Feedback studies have made on interactive CBIR.

These approaches also have some problems like repetition of same image called as redundant browsing and when user submit the query image it divert into multiple path called as exploration convergence. Most of image retrieval techniques try to satisfy user in first query image and it gives the very precise image to users That is, existing techniques refine the query images again and again by analyzing the specific relevant images picked up by the users side. For very complex image it is very difficult to retrieve images in very short retrieval, so it require long series of feedback so that that user satisfactory image will displayed[1]. In fact, it is not practical in real applications like online image retrieval in a large-scale image database. Fig. 1 illustrates the problem of exploration convergence. In Fig. 1, suppose that two users query with the same image whose concept consists of ―”car and ―sunset”. In this example, however, the aimed concept for Query 1 and Query 2 is ―”car” and ―”sunset”, respectively. After a set of feedbacks for Query 1 and Query 2, two different moving paths will be produced since they will lead to images of aimed concepts, respectively. This problem is called as visual diversity. In this case, if the compound concept to aim at consists of car,sunset,and sunset and car, it is not easy for traditional CBIR methods to capture the user’s intention. For query point movement, this problem will result in that the features would converge toward the specific point in the feature space during the query session.

query n.

Figure 1: Example for the Problem of Exploration Convergence

Hence, it is still hard to cover the concepts of car, sunset, and sunset car even by performing the weighted K-Nearest Neighbors search To resolve the problems, a novel method named based Navigation Patterns for efficient Relevance Feedback is used to achieve the high quality of CBIR retrieval with RF by using the discovered based navigation patterns. According to the discovered patterns, the users can obtain a set of relevant images in an online query refinement process. Thus, the problem of redundant browsing is successfully solved. In terms of effectiveness, navigation-pattern-based search algorithm (NPRF Search) merges three query refinement strategies, including Query Reweighting , Query Point Movement , and Query Expansion , to deal with the problem of exploration convergence.

2. RELATED WORK

To avoid the problem of redundant browsing and problem exploration following strategic are given.

2.1 Query Reweighting (QR)

The notion behind QR is that, if the user submit the query image to system it give result dynamically and then user is not satisfies then it dynamically update image and search the image in system ,gives satisfactory result to user. QR allow to user to submit image query to refine his/her search through set of relevance feedback. In this work, the feature weights are dynamically updated to connect low-level visual features and high-level human concepts. KNN learns the user’s query from positive and negative examples by reweighting the important features. For such type of approach, no matter how the weighted distance function is adapted, the diverse visual features extremely limit the effort of image retrieval techniques.

2.2 Query Point Movement (QPM)

Another method for incresing the accuracy of image retrieval is the query point move toward the contour of the user’s preference in feature space of image. Query Point Movement regards multiple positive examples as a new query point at each feedback in search method. After several forceful changes of location and contour in query point , the query point should be close to a convex region of the user’s interest[2].

2.3 Query Expansion (QE)

The technique QR and QPM cannot overcome the drawback in traditional CBIR method so Query expansion is used so far. For this reason, this paper describe method that groups the similar relevant points into various clusters, and selects good representative points from these clusters to construct the multipoint query. Query cluster [2] intends to handle the disjunctive queries by employing adaptive classification and cluster merging methods. The effectiveness of QEX is better than those of QPM and QR. QEX brings out higher computation cost and more feedbacks in RF[2].

2.4 Hybrid RF

Another type of RF approach emphasizes the integration of various search strategies. exploiting knowledge from the long-term experience of users can facilitate the selection of multiple RF techniques to get the best results. But the derived problems are: the selection of optimal RF technique cannot avoid the overhead of long iterations of feedback. Also, the visual diversity existing in the global feature space cannot be resolved with an optimal RF technique alone.

3. NAVIGATION PATTERN FOR RELEVANCE FEEDBACK

3.1 Problem Definition

Extracting multimedia data from the large multimedia repository suffer from problem such as redundant browsing and exploration convergence. Whenever the user query the database the resultant data is irrelevant with the user’s query and it takes long iterations of feedback to produce the result. The goal is to assist the search strategy in efficiently hunting the desired images.

3.2Overview of NPRF

NPRF contains the discovered navigation patterns and three Relevance Feedback techniques to achieve efficient and effective retrieval of images as shown in Fig.2. The task is divided into two major operations, offline knowledge discovery and online image retrieval.

3.2.1 Online retrieval of image with Initial Query Processing Phase: Without considering the feature of image like color, shape etc, this phase extracts the visual features like color shape etc from the original query image to find the identical images. Afterward, the good examples (also called positive examples) selected by the user are further analyzed by algorithm at the first feedback (also called iteration 0).

Image Search Phase: Behind the search phase, extending from the one search point to multiple search points by integrating the navigation patterns algorithm and the proposed search algorithm NPRF Search. A new query point at each feedback is generated by the preceding positive examples. Then, the nearest images to the new query point can be found by enhancing the weighted query. The search procedure doesn’t stop unless the user is satisfied with the retrieval of satisfies image.

3.2.2 Offline Knowledge Discovery: Knowledge Discovery Phase : Learning from users’ behaviors in image retrieval method can be viewed as one type of knowledge discovery phase. Consequently, this phase focus on the construction of the navigation model by discovering the implicit navigation patterns from users’ browsing behaviors. This navigation model can provide image search with a good support to image browsing paths.

Data Storage Phase: The datasets in this phase consist of the knowledge of images contains in warehouse, which store integrated, time variant useful, and nonvolatile collection of useful data including images, navigation patterns, log files, and image features. The knowledge warehouse is very needful to improve the quality of image retrieval method.

3.3.1 Offline Knowledge Discovery: Web image retrieval, the user has to submit a query term to the search engine, so-called textual-based image search. Then the user can obtain a set of most relevant web images according to the metadata or the browsing log. However, if the result does not satisfy the user, the query refinement can be easily incorporated into the query procedure. The usage log of CBIR suffers mainly on how to generate and utilize the discovered patterns. The data structure can be viewed as a hierarchy, including positive images, query points, and clusters. A query session contains a set of iterative feedbacks (iterations), which is referred to a navigation path. At each feedback, the positive examples, which indicate the results picked up by the user, are used to derive a referred visual query point by averaging the positive visual features. Finally, the query sessions, iterations, positive examples, and visual query points are stored .If the original log data are ready; the next task is to discover navigation patterns from the original log data. For navigation patterns mining, the frequent item sets are mined from navigation-transactiontable. The transformed log table is also divided into several sub tables.

3.3.1 Data Transformation Data Transformation simplifies both the description of visual query points and the discovery of navigation patterns. Without data transformation, all positive images of each query session in the database are considered. If all positive images The aim of data transformation is to generate Query Point Dictionary (QPD) to reduce the kinds of items on the transaction list. In this phase, the transformed log are considered for navigation pattern mining, too many items make the frequent item sets hard to find. Also, the mining cost is expensive. table is first generated by the logged query sessions containing query session id, iteration number, positive images, and visual query point number.Once a query point is projected onto the QPD, the referred item number is stored into the transform log table. The transformed log table has to be partitioned into three tables for various needs, including QP table, Navigation-transaction table, and Partitioned Log table. Navigation-transaction table is used for navigation patterns mining.

Fig.2. Example of navigation pattern trees.

3.3.2 Navigation Pattern Mining

This stage focuses on the discovery of relations among the users’ browsing behaviors on RF. In NPRF approach, the users’ common interests can be represented by the discovered frequent patterns (alsocalled frequent item sets). Throughthese navigation patterns, the user’s intention can beprecisely captured .The task is decomposed into two steps:

Step 1: Construction of the navigation transaction table. To exploit valuable navigation patterns, all query sessions in the transformed log table are collected as the navigation transaction table.

Step 2: Generation of navigation patterns. This operation concentrates on mining valuable navigation patterns to facilitate online image retrieval.

3.3.3 Pattern Indexing

In this stage, build the navigation pattern tree with the discovered navigation patterns. As shown in Fig.3, the navigation patterns can be regarded as the branches of the navigation pattern tree. Once the navigation patterns are generated, the query item in each navigation pattern is used as a seed (called query seed) to plant the navigation pattern tree.

C:\Users\RAJESH\Pictures\algo3.PNG

Fig. 3.Procedure for offline knowledge discovery.

A tree contains a number of navigation paths, and each node of the paths stands for an item consisting of several visual query points. A visual query point indicates a set of positive images. Based on the navigation pattern tree, the desired images can be captured without repeating the scan of the

The iterative search method can be decomposed into several steps as follows:

1. Generate a new query point from image submitted by user by averaging the visual-features of positive examples.

2. Find out the matching navigation pattern trees by determining the nearest query seeds (root).

3. Find the nearest leaf nodes (terminations of a path) from the matching navigation pattern trees.

4. Find out the top most relevant visual query points from the set of the nearest precise pixel..

5. Finally, retrieve the relevant images and that returned to the user.

4. EXPERIMENTS

After implementing the project the query logs can be obtained by performing query point movement. Navigation patterns are obtained by using the pattern discovery mechanism. The knowledge discovered from the navigation patterns can be enhanced once the query is submitted to NPRF. To analyze the effectiveness of the proposed approach, two major criteria, that are precision and coverage are used to measure the related experimental evaluations. They are defined as

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where correct is the positive image set to the query image at each feedback, retrieved is the resulting image set exploited by the proposed approach at each feedback, ac_correctis the union set of all correct during a query session, and relevant is the ground truth. The criterion precision delivers the ability for hunting the desired images in user’s mind and the coverage represents the ability for finding the accumulated positive images in a query session.

TABLE 1 :The Experimental Parameter Settings

In fig.3 the first parameter we concerned is the number of clusters cl .fig reveals that the precision slightly degrades as the number of clusters increases

In fig 4 ,The second parameter we tested is the number of logs and Fig. depicts that, the larger the number of logs, the higher the average precision and the higher the retrieval cost

Fig.4The average precisions of different clusters for data set

Fig.5The average precisions of different numbers of log-transaction for data set

5. CONCLUSION

Proposed a relevance feedback technique based on NPRF (navigation-pattern based relevance feedback) approach for Speedup the ordinal correlation measure. On one hand, the navigation patterns derived from the users’ long- term browsing behaviors are used as a good support for minimizing the number of user feedbacks. Within a very short term of relevance feedback, the navigation patterns can assist the users in obtaining the global optimal results and bring out more accurate results than other well-known approaches. As a result, traditional problems such as visual diversity and exploration convergence are solved. In addition, the involved methods for special data partition and pattern pruning also speed up the image exploration. The experimental results reveal that the proposed approach NPRF is very effective in terms of precision and coverage. Within a very short term of relevance feedback, the navigation patterns can assist the users in obtaining the global optimal results.


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